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eOLLA: an enhanced outer loop link adaptation for cellular networks
Francisco Blanquez-Casado1,
Gerardo Gomez1,
Maria del Carmen Aguayo-Torres1 &
Jose Tomas Entrambasaguas1
Link adaptation (LA) process is a core feature for the downlink of 3GPP long-term evolution (LTE) and LTE-advanced (LTE-A). Through a channel quality indicator (CQI), the receiver suggests to the base station (BS) an appropriate modulation and coding scheme (MCS) according to the current channel conditions. In order to overcome any non-ideality in this process, the outer loop link adaptation (OLLA) algorithm is used to adaptively modify the mapping from signal-to-noise ratio (SNR) to CQI. OLLA basically modifies the measured SNR by an offset, according to whether data packets are received correctly or not, in order to adjust the average block error rate (aBLER) to a target. Although the OLLA technique has been extensively used, there exists a lack of analysis in the literature about its dynamics and convergence conditions. In this paper, a deep analysis of this algorithm has been carried out in order to cover this gap. From this analysis, we propose a new approach to the OLLA, the enhanced OLLA (eOLLA), which is able to adaptively modify its step size as well as to update its offset according to the reception conditions even if no data packets have been received. Thus, for LTE- and LTE-A-realistic scenarios, simulation results show that the proposed eOLLA outperforms the traditional OLLA, achieving a performance gain of up to a 15 % in terms of throughput.
The adaptive modulation and coding (AMC) process carried out in the link adaptation (LA) is a crucial part of current wireless communication systems. This technique allows to increase the data rate that can be reliably transmitted [1] and has been adopted as a core feature in cellular standards such as long-term evolution (LTE) and LTE-advanced (LTE-A) [2].
In the LTE and LTE-A downlink AMC procedure [2], the user equipment (UE) has to suggest to the base station (BS) an appropriate modulation and coding scheme (MCS) to be used in the next transmission in order to keep the block error rate (BLER) below a target. The proposed MCS is signaled from the UE by means of a channel quality indicator (CQI). Typically, each CQI is associated with a particular signal-to-noise ratio (SNR) interval; hence, MCSs are selected by mapping the estimated instantaneous SNR into its corresponding SNR interval, defined by an upper and a lower threshold.
A static selection of the values for the AMC thresholds does not perform well in practical implementations as link conditions are inherently variant. It is usual to adjust these thresholds by means of the well-known outer loop link adaptation (OLLA) technique, which was first proposed in [3]. Basically, OLLA modifies the SNR thresholds by an offset [4, 5] which can be positive (making the MCS selection more robust) or negative (when the CQI selection was too strict). This offset is continuously updated based on the reliability of the received data blocks so that the average BLER is kept as close as possible to a predefined target.
Although there are works devoted to OLLA in the literature [6, 7], they typically address its performance from simulations, and the lack of a comprehensive analysis of its behavior in the literature is noticeable. Furthermore, to the best of our knowledge, previous works do not analyze the conditions under which the OLLA technique works properly. The first aim of this work is to cover this gap by carrying out a deep study of the OLLA technique.
From this study, improvements in the implementation of the traditional OLLA can be inferred. Thus, in this paper, a different approach to the OLLA technique is proposed, the enhanced OLLA (eOLLA), which can significantly improve the performance of the traditional OLLA.
This paper is organized as follows. In Section 2, the AMC model for LTE used in this work is described, and then a detailed description of the OLLA is carried out, including a study of its convergence conditions and its performance. In Section 3, the proposed eOLLA is presented. Finally, Section 4 shows a comparison between both the traditional OLLA and the proposed eOLLA in realistic scenarios based on the downlink of LTE and LTE-A, and some concluding remarks are given in Section 5.
Outer loop link adaptation (OLLA)
To perform the AMC [1], the instantaneous SNR γ is estimated at the UE to determine the current fading region ℜ i and, consequently, the transmission rate R i (bits/symbol). At the UE, this instantaneous SNR is mapped into a certain CQI value, which is fed back to the BS.
The set of SNR thresholds {Ψ i } i=0,1,..,n defines the intervals to map the estimated instantaneous SNR into its corresponding CQI, with Ψ 0 representing the minimum required SNR for transmission (outage condition) and Ψ n =∞. These thresholds have been designed in order to accomplish certain constraints, such as limiting the maximum instantaneous BLER (iBLER) or defining an average BLER (aBLER) target. The latter approach (based on aBLER) is the one adopted by most wireless technologies like LTE [1]. Therefore, our description will be focused on the aBLER scenario.
For a certain average SNR Γ, the average BLER under AMC can be evaluated as
$$ {}aBLER (\Gamma, \{\Psi_{i}\}) = \sum\limits_{i = 0}^{n - 1} \int_{\Psi_{i}}^{\Psi_{i + 1}} {i}BLER_{i}^{AWGN} (\gamma){p}^{o} (\Gamma, \gamma)d\gamma, $$
being \({\textit {i}BLER_{i}^{AWGN}}(\gamma),\) the instantaneous BLER for a given MCS i over an additive white Gaussian noise (AWGN) channel and p o(Γ,γ) the probability density function (PDF) of the instantaneous SNR conditioned to transmission.
In this, work we have assumed an uncorrelated Rayleigh channel for the analysis. Thus, the PDF of the instantaneous SNR for a certain average SNR Γ is given by an exponential function [1]
$$ {p}(\Gamma,\gamma) =\frac{1}{\Gamma}e^{-\gamma}\!\!/\!_{\Gamma}. $$
Then, the instantaneous SNR conditioned to transmission is given by:
$$ p^{o}(\Gamma,\gamma) = \left\{\begin{array}{l} \frac{1}{{{A}\left(\Gamma,{\Psi_{0}} \right)\Gamma }}e^{-\gamma}\!\!/\!_{\Gamma},\gamma >\Psi_{0} \\ 0,\text{else} \\ \end{array}\right. $$
where A(Γ,Ψ 0) is the probability of not being in outage, that is,
$$ {A}\left({\Psi_{0}}\right) = \int_{\gamma = \Psi_{0}}^{\infty} \frac{1}{\Gamma}{e^{-\gamma}\!\!/_{\Gamma}}{_{d{\gamma}}} = {e^{-\Psi_{0}}\!/\!{_{\Gamma}}}. $$
According to Eq. (1), in order to evaluate the aBLER, it is necessary to have at our disposal an expression for \(\textit {i}BLER_{i}^{AWGN}\), but to the best of our knowledge, this is not available in the literature when turbo coding is used. Moreover, the exact value of the iBLER strongly depends on the specific decoder implementation [8]. Nevertheless, since the iBLER metric represents the probability of being in one of two states {error,no−error}, we propose the use of binary logistic regression [9]. This regression is a binary classifier based on one or more input variables. Thus, it is a useful tool to model iBLER curves for each MCS i over AWGN channels, for a given instantaneous SNR γ, by means of binary logistic functions as:
$$ {i}BLER_{i}^{AWGN} (\gamma) \approx f_{i}(\gamma)= \frac{1}{1 + e^{-\alpha_{i_{0}} \gamma - \alpha_{i_{1}} }}, $$
where the \(\alpha _{i_{0}}\) and \(\alpha _{i_{1}}\) values (see Table 1) are to be found from the logistic regression over results of the actual decoder implementation (see Section 3.2 for further details). The accuracy of logistic functions after the curve-fitting process is shown in Fig. 1 for the whole set of CQI values of LTE [2], where solid lines represent the analytic BLER curves whereas simulation results of a soft output Viterbi algorithm (SOVA)-based turbo decoder [10] are marked with circles.
Curve fitting of logistic functions to model iBLER curves (CQIs 1–15)
Table 1 Values of \(\alpha _{i_{0}}\) and \(\alpha _{i_{1}}\) of modeled iBLER curves
A static selection of the values for the AMC thresholds Ψ i does not perfectly adjust the aBLER to a target since link conditions are inherently variant. Thus, in order to meet the aBLER T , different sets of SNR thresholds {Ψ i } i=0,1,..,n should be used for different link conditions. This can be modeled by rewriting each SNR threshold Ψ i =γ i ·θ, being γ i an initial value, and θ an offset that must be designed to meet the aBLER T . Then, Eq. (1) is modified as
$$ {}aBLER (\Gamma,\{\gamma_{i}\}, \theta) = \sum\limits_{i = 0}^{n - 1} \int_{\gamma_{i}\theta}^{\gamma_{i + 1}\theta} {iBLER_{i}^{AWGN}} (\gamma){p}(\gamma)d\gamma. $$
The previous process is typically performed in practical implementations by the outer loop link adaptation (OLLA) technique [3–5]. The traditional OLLA operation consists in dynamically modifying the value of the offset according to whether the previously transmitted data packet has been correctly received or not. In LTE and LTE-A, this information is extracted from the cyclic redundancy code (CRC) [11]. Thus, OLLA can be seen as a discrete time system in which, each time k a CRC is received, the value of the offset θ[k] is updated according to the following equation [5]
$$ \theta_{dB} \left[ k \right] = \theta_{dB} \left[ {k - 1} \right] + \Delta_{up} \cdot e\left[ k \right] - \Delta_{down} \cdot \left({1 - e\left[ k \right]} \right), $$
θ dB [k]=10·log 10(θ[k]).
e[k] an error indicator, whose value is 0 if the CRC is correct, or 1 if not. It corresponds to a dichotomous random variable whose average is the aBLER.
Δ up and Δ down constant increment and reduction values, respectively, of the offset, in decibels. These two values are positive and should satisfy Eq. (8) in order to meet the aBLER T [5]. This condition will be justified in Subsection 2.1.
$$ aBLER_{T} = \frac{1}{1 + \frac{\Delta_{up}}{\Delta_{down}}}. $$
In addition to that, the value of Δ down cannot exceed an upper limit \(\Delta _{down_{\textit {max}}}\) in order to ensure the proper working of the OLLA. This limit is given by the next equation, which is justified in Subsection 2.1.
$$ \Delta_{down_{max}} < \frac{2e \cdot aBLER_{T}}{-\alpha_{1}}. $$
SNR threshold values are now modified at each instant k by a discrete offset, so they can be expressed as
$$ \Psi_{i} \left[ k \right] = \gamma_{i} \cdot \theta \left[ k \right]. $$
To sum up, the OLLA operation consists in increasing the offset value (and so increasing the value of the SNR thresholds) when error happens, and decreasing them when transmissions are correct. Therefore, θ[k] values higher than 1 increase transmission robustness, while values lower than 1 decrease it.
OLLA convergence in average
It should be noticed that OLLA dynamics imply that the offset value θ dB [k] is continuously being updated by adding Δ up (if error) or subtracting Δ down (if not). Thus, it will never converge to a single value. However, the offset θ presented in (6) is a single value that ensures the aBLER T for stationary link conditions. The reason of this difference is that θ is used in an averaging process, while in case of the OLLA, θ dB [k] is an instantaneous value. Therefore, to study the convergence of the OLLA process, averaged values must be considered.
In this subsection, the convergence in average of the OLLA algorithm for stationary link conditions, as well as the conditions that should be satisfied in order to achieve this convergence is studied. For this purpose, mathematical expectation can be applied to (7) to find the averaged \(E\left [ {\theta _{\textit {dB}} \left [ k \right ]} \right ] = \theta _{dB_{k}}\) value at k, obtaining the following equation after reordering
$$ {}\theta_{dB_{k}} = \theta_{dB_{k - 1}} + \left({\Delta_{up} + \Delta_{down}} \right) \cdot E\left[ {e\left[ k \right]} \right] - \Delta_{down}. $$
Note that since the average value of θ dB [k] depends on k, it is not an ergodic process. Only when k→∞ the process can be considered ergodic. Expression (11) corresponds to a difference equation, i.e., there is a recurrence relation between the terms in the form of \(\theta _{dB_{k}} = T\left (\theta _{dB_{k-1}} \right)\), being T(θ dB ) the recurrence function. Hence, according to the Banach fixed-point theorem [12], when k→∞ the equation will converge to the only value such that \(\theta ^{o}_{\textit {dB}}=T\left (\theta ^{o}_{\textit {dB}}\right)\) if certain conditions are fulfilled. The following equation is obtained when convergence in average is reached
$$ \left({\Delta_{up} + \Delta_{down}} \right) \cdot E\left[ {e\left[ k \right]} \right] - \Delta_{down} = 0. $$
Note that E[e[k]] is the average number of errors, i.e., the aBLER. Thus, in order to ensure that the converged offset value \(\theta ^{o}_{\textit {dB}}=10\cdot log_{10}\left (\theta ^{o}\right)\) meets the aBLER T , the aBLER should be forced to this value, obtaining the the following relation
$$ aBLER_{T} = E\left[ {e\left[ k \right]} \right] = \frac{1}{{1 + \frac{{\Delta_{up} }}{{\Delta_{down} }}}}. $$
which was already presented in (8), and now it has been justified. Therefore, under certain link conditions, the OLLA algorithm converges in average to a value θ o which is the value of θ to be introduced in (6) in order to meet the aBLER T .
Equation (13) manifests that there are infinite suitable combinations of Δ up and Δ down that ensures the aBLER T . However, the specific values of Δ up and Δ down must accomplish the convergence criteria required by the Banach fixed-point theorem applied to (11), whose two sufficient conditions are described next.
First Banach fixed-point theorem condition
The function T(θ dB ) should be a contraction mapping, that is, it should satisfy that
$$ T\left({\theta_{dB}} \right) \in \left[ {\theta_{min},\theta_{max} } \right],\forall \theta_{dB} \in \left[ {\theta_{min},\theta_{max} } \right]. $$
It can be easily shown that this condition is always fulfilled by the OLLA. Firstly, since E[e[k]] is the average number of errors, i.e., the aBLER, its range of values are comprised between 0 and 1. Thus, there will be a value θ dB =θ min low enough to ensure that E[e[k]]=1, which means that T(θ min )=θ min +Δ up . Then, as θ dB increases, the value of E[e[k]] will decrease until θ dB raises a value θ max high enough to ensure that E[e[k]]=0, which means that T(θ max )=θ max −Δ down . As a result of that, the values of T(θ dB ) will be comprised between
$$ \begin{aligned} T\left({\theta_{dB}} \right) \in \left[ {\theta_{min} + \Delta_{up},\theta_{max} - \Delta_{down}} \right] \\ \in \left[ {\theta_{min},\theta_{max}} \right],\forall \theta_{dB} \in \left[ {\theta_{min},\theta_{max}} \right]. \end{aligned} $$
Second Banach fixed-point theorem condition
The second condition that the function T(θ dB ) should fulfill is that
$$ \left| {T^{\prime}\left({\theta_{dB}} \right)} \right| < 1, ~~ \forall \theta_{dB} \in \left[ {\theta_{min},\theta_{max}} \right] $$
being T ′(θ dB ) the first order derivative of T(θ dB ). To check this condition, it is necessary as an expression for E[e[k]], this is, for the aBLER. This expression is provided by (6), and it depends on the specific parameters of the binary logistic regression carried out to model the \(\textit {i}BLER_{i}^{AWGN}\) curves. In order to have a more tractable expression, aBLER(Γ,{γ i },θ) values have been obtained numerically for an average SNR value of Γ=15dB and certain \(\textit {i}BLER_{i}^{AWGN}\) curves (see Section 5 for further details) for different θ dB values according to (3), (5), and (6). These values have been obtained based on the parameters of Table 2 and are presented in Fig. 2 (solid line).
Curve fitting of modified logistic function to model aBLER for Γ= 15 dB
Table 2 Parameters for OLLA convergence
The shape of the aBLER curve is similar to the iBLER. Thus, it could also be modeled by means of logistic regression, which was introduced in Section 2. In this case, we propose to model this curve by means of a modified binary logistic function, which introduces a parameter s which controls the slope, as
$$ {}E\left[ {e\left[ k \right]} \right] = aBLER(\Gamma,\theta_{dB}) \approx f_{m}(\theta_{dB})=\frac{1}{\left(1 + e^{- \alpha_{0} \theta_{dB} - \alpha_{1}} \right)^{s}}. $$
After finding the values of α 0, α 1, and s by logistic regression, the resulting fitted curve is shown in Fig. 2 as a dashed line. It can be seen how the proposed expression perfectly fits with the simulated results.
By using the proposed modified logistic function, it can be evaluated by the expression of T(θ dB ), according to (11), as
$$ T\left({\theta_{dB}} \right) = \theta_{dB} + \left({\Delta_{up} + \Delta_{down}} \right) \cdot f_{m}\left(\theta_{dB}\right) - \Delta_{down} $$
Next, the condition to ensure that |T ′(θ dB )|<1 is derived from (13), (17), and (18). First, we find the value of T ′(θ dB ) as
$$ T^{\prime}\left({\theta_{dB}} \right) = 1 + \left({\Delta_{up} + \Delta_{down}} \right) \cdot f_{m}^{\prime}\left({\theta_{dB}} \right). $$
Since f m (θ dB ) is a decreasing function, that is \(f_{m}^{\prime }\left ({\theta _{\textit {dB}}} \right) \le 0\), and both Δ up and Δ down are positive, the following condition must be guaranteed in order to ensure the convergence condition
$${} {\fontsize{8.5pt}{9.3pt}\selectfont{\begin{aligned} \left({\Delta_{up} + \Delta_{down}} \right)\cdot\left|\, {f_{m}^{\prime} \left({\theta_{dB}} \right)} \right| < 2 \Rightarrow \left|\, {f_{m}^{\prime} \left({\theta_{dB}} \right)} \right| < \frac{2}{\left({\Delta_{up} + \Delta_{down}} \right)}. \end{aligned}}} $$
Thus, we find the maximum of \(\left |\, {f_{m}^{\prime }\left ({\theta _{\textit {dB}}} \right)} \right |\), i.e., the values for which \(f_{m}^{\prime \prime }\left ({\theta _{\textit {dB}}} \right) = 0\), to ensure that the condition imposed by (20) is fulfilled. After reordering, we find that the offset value \(\theta _{dB_{\textit {max}}}\) for which the maximum of \(\left |\, {f_{m}^{\prime }\left ({\theta _{\textit {dB}}} \right)} \right |\) is achieved as
$$ \theta_{dB_{\max} } = \frac{1}{{\alpha_{1} }} \cdot \left({\ln \left({\frac{1}{s} + \alpha_{0}} \right)} \right) $$
and therefore, the maximum value of \(\left |\, {f_{m}^{\prime }\left ({\theta _{\textit {dB}}} \right)} \right |\) is
$$ \left|\, {f_{m}^{\prime}\left({\theta_{dB_{\max} }} \right)} \right| = \left| {\alpha_{1}} \right| \cdot \frac{1}{\left({1 + {^{1}\!\!/\!_{s}}}\right)^{s + 1} }. $$
As it was said before, the fact that f m (θ dB ) is a decreasing function implies that s≥0. It can be easily seen that higher values of f m (θ dB ) are achieved as s is increased. Then, we get an upper bound of \(f^{\prime }_{m} \left ({\theta _{\textit {dB}}} \right)\) when s→∞ as
$$ \left|\, {f^{\prime}_{m_{\max}}} \right| = \frac{\left| {\alpha_{1}} \right|}{e}. $$
Finally, from (13), (20), and (23), we find that the second condition to ensure the OLLA convergence, which was already presented in (9), is given by
$$\Delta_{down_{max}} < \frac{{2e \cdot aBLER_{T} }}{{-\alpha_{1} }}. $$
Thus, convergence is ensured if the decrement step does not raise a maximum value, which is determined by α 1, that is, by the curve of aBLER for a certain Γ value of mean SNR.
According to previous results, there is a range of Δ down values, and so of Δ up values, that ensures convergence. In Fig. 3, the convergence process when different values of Δ down are used is presented, for given link conditions whose parameters are listed in Table 2. These parameters have been obtained for the same conditions in Fig. 2.
OLLA convergence in average for different Δ down values; aBLER t =0.1
Figure 3, shows how the converged value \(\theta _{{dB}_{k}}\phantom {\dot {i}\!}\) raises its final value \(\theta ^{o}_{\textit {dB}} \approx - 0.8\) (dashed line) faster as the size of Δ down is higher. Note that this convergence value is the same that obtained in Fig. 2 by using (17). Once Δ down exceeds \(\Delta _{down_{\textit {max}}}\), whose value is 0.52 for the proposed scenario, the OLLA begins to diverge, this divergence being more remarkable as Δ down increases. Therefore, it would be advisable to choose the highest possible value of Δ down in order to achieve convergence as fast as possible.
OLLA performance
In this subsection, the OLLA performance is analyzed under stationary link conditions. As it will be shown, when convergence, the instantaneous offset value θ dB [k] fluctuates around the converged value \(\theta ^{o}_{\textit {dB}}\) following the instantaneous channel variations. The amplitude of these fluctuations will be related to the values of Δ up and Δ down . Thus, high values of these two parameters, even if they guarantee the OLLA convergence, may lead to a high variance in the instantaneous offset value that could degrade the OLLA performance. On the other hand, too small values of Δ up and Δ down may imply that the OLLA could not follow the channel variations if they are too fast.
In Fig. 4, the instantaneous values of θ[k] are shown for the same simulation conditions of Table 2, for a set of values of Δ down that guarantee convergence (\(\Delta _{\textit {down}}<\Delta _{down_{\textit {max}}} = 0.52\phantom {\dot {i}\!}\)). These results show that for the lowest Δ down value, it takes several steps to the OLLA to reach a state for which the offset is stabilized around a certain value, which is the converged value \(\theta ^{o}_{\textit {dB}} \approx - 0.8\) of Fig. 3. As the value of Δ down increases, it takes less steps to the OLLA to stabilize; however, the variance of the offset also increases, which may cause a degradation in the OLLA performance.
Instantaneous offset for an uncorrelated flat Rayleigh channel
As it was previously stated, a high variance of the OLLA offset can degrade its performance. This fact is shown in Table 3, which presents the achieved throughput and the aBLER for the different Δ down values, under the same condition as in Fig. 4. Results reveal that as the size of the step increases, there is a reduction of the spectral efficiency, although all configurations meet the aBLER T . The reduction of spectral efficiency for the maximum Δ down value that guarantees convergence (0.5 dB) is about 8 % with respect to the step value with better throughput performance (Δ down =0.01 dB).
Table 3 System performance for OLLA with different Δ down sizes
The reason for this throughput degradation for high step values is that they may cause that when an error happens, the OLLA selects a more robust MCS than the required to ensure the aBLER T .
To sum up, while high values of the OLLA steps are desirable to achieve a fast convergence, low step values lead to higher throughput once convergence is achieved, since it provides a better adjustment of the MCS. Thus, the selection of the values of Δ up and Δ down should be carried out carefully in order to optimize the OLLA performance.
Enhanced outer loop link adaptation
In the previous section, the traditional OLLA algorithm has been deeply studied, showing that a key factor to optimize the performance is the appropriate selection of its step size. However, the way this selection should be carried out is not clear. Moreover, the performance of the OLLA also depends on the specific implementation of features at the receiver such as the turbo decoder or the channel estimation method.
In addition to that, another problem is that the OLLA only updates its offset every time k an ACK/NACK is received, i.e., when a transmission is done. In real scenarios, it is usual not to have full-buffer traffic but a discontinuous transmission with variable traffic. In these cases, the UE may be most of the time in idle mode [13]. Therefore, a combination of variant channel conditions together with these kind of traffic patterns could cause a performance degradation since the OLLA may not be able to update its offset fast enough to follow the channel variations.
Proposed eOLLA
In this section, we propose a modification of the traditional OLLA algorithm, the enhanced outer loop link adaptation (eOLLA), which is able to adapt the size of the steps according to the convergence status of the system. Furthermore, the proposed eOLLA updates its offset independently of whether a transmission is carried out or not.
Since the error indicator e[ k] used in the traditional OLLA can be seen as a one bit instantaneous BLER estimator (1 if error, 0 if not), this value could be replaced by a more accurate instantaneous BLER estimation. Thus, to carry out the implementation of the proposed eOLLA, a model for the instantaneous BLER of each MCS i over AWGN channels for instantaneous SNR, \(\textit {i}BLER_{i}^{AWGN}(\gamma \!)\), is needed.
The proposed eOLLA implemented from (7) is as follows
If an ACK/NACK is received, update the corresponding \(\textit {i}BLER_{i}^{AWGN}\) model according to the MCS i used in this transmission (optional).
Estimate the instantaneous SNR value γ[ t].
At each Transmission Time Interval (TTI) t, calculate the instantaneous BLER value \(B[t] = \textit {i}BLER_{i}^{AWGN} (\gamma [\!t]\!)\).
Get the offset value as
$$ \begin{aligned} \theta_{dB} \left[ t \right] = \theta_{dB} \left[ {t - 1} \right] + \Delta_{up} \cdot B[t] - \Delta_{down} \cdot \left({1 - B\left[ t \right]} \right). \end{aligned} $$
In the eOLLA algorithm description, it should be noticed that the index k of Eq. (7) of the traditional OLLA description has been replaced by the index t in Eq. (24). This means that the offset of the eOLLA will be updated every TTI t (every time a transmission can be potentially carried out) instead of every time k a CRC is received (every time a transmission is carried out). Then, in case of discontinuous transmissions, the proposed eOLLA updates its offset more frequently than in case of the traditional OLLA, which implies that the eOLLA is able to follow easily the temporal variations of the channel, thus improving the AMC performance.
This continuous updating of the eOLLA offset can be easily performed in LTE since there is an estimation of the SNR available every TTI t. In the downlink of LTE and LTE-A, the BS transmits reference signals (RS) [2] at each subframe, in both connected and idle modes. Thus, every UE is able to perform SNR estimation from this RS at each TTI, and so to update its eOLLA offset.
The fitting process referred in step (1) of the proposed algorithm is used to adjust the instantaneous BLER model to the specific system implementation used. This process is required just in case a fitted model is not available.
For the purpose of a better understanding of the eOLLA behavior, step (4) can be rewritten according to (13) as
$$ \theta_{dB} \left[ t \right] = \theta_{dB} \left[ {t - 1} \right] + \Delta_{down} \cdot\left({\frac{B\left[ t \right]}{aBLER_{T}} - 1} \right). $$
From the previous equation, it can be deduced that the proposed eOLLA adapts the increment to be applied to the offset according to the difference between the estimated instantaneous BLER B [ t] and the target aBLER. Then, if B [ t]=0, the offset will be decreased by Δ down , thus decreasing the robustness of the next transmission in the same way than in the traditional OLLA. As B [ t] increases, the size of the decreasing step reduces, and so increases the robustness of the next transmission until B [ t]=aBLER T , which is the equilibrium point. At this point, no step will be applied to the offset, since if the eOLLA remains in this state the average BLER will meet the target aBLER. In case that B [ t] exceeds the aBLER T , the offset will be increased, and thus the robustness of the next transmission, until B [ t]=1. In this case, the offset will be increased by Δ up , as in the traditional OLLA.
Hence, for the eOLLA, if the offset value leads to either a very high or a very low instantaneous BLER, high-step values are used in order to correct this situation as fast as possible. Then, as the iBLER is closer to the average target BLER, lower step sizes are used to reduce the variance of the MCS and maximize the throughput.
Since E[B [ t] ] is the same than E[e[ t] ], the convergence analysis of the traditional OLLA carried out in Subsection 2.1 can be applied to the eOLLA. Thus, to ensure the convergence of the eOLLA, condition (9) must be also satisfied. Note that under stationary conditions, the offset value for which E[B [ t] ]=aBLER T corresponds to the convergence value \(\theta ^{o}_{\textit {dB}}\) previously described.
Logistic regression implementation
In previous section, we stated that an expression of the instantaneous BLER \(\textit {i}BLER_{i}^{AWGN} (\gamma [\!t]\!)\) was required to model the eOLLA algorithm. As described in Section 2, instantaneous BLER for MCS i over AWGN channels can be modeled by means of binary logistic functions.
Therefore, logistic regression can be used in the eOLLA algorithm (step 1) to find out the values of \(\alpha _{i_{0}}\) and \(\alpha _{i_{1}}\) in Eq. (5) that better fit with the \(\textit {i}BLER_{i}^{AWGN}\) curve. In practice, this logistic regression can be easily implemented by the well-known gradient descend algorithm [14]. This algorithm is typically used to minimize a cost function J(α) as follows
$$ \alpha : = \alpha - \lambda \frac{\partial}{\partial \alpha }J\left(\alpha \right) $$
where λ is a parameter that controls the convergence speed. Thus, first of all, it is necessary to provide the logistic regression cost function, given by [15]
$$ J\left(\alpha \right) = y \cdot \log \left(\,{f_{\alpha} \left(x \right)} \right) - \left({1 - y} \right) \cdot \log \left({1 - f_{\alpha} \left(x \right)} \right) $$
being x, the input to the system, y, the class the input x falls into (with only two possible values, 0 or 1), and f α a binary logistic function to model the mapping of x into a class y. Thus, to translate the generic logistic regression function to our problem, we associate the input x to the instantaneous SNR value γ, the classification result y as an error indicator e, and f α as the binary logistic function of (5). Then, applying partial derivation of the cost function, the resulting logistic regression process for the proposed eOLLA is given by:
$$ {\begin{aligned} \left\{ \!\!\begin{array}{l} \alpha^{k}_{i_{0}} = \alpha^{k-1}_{i_{0}} - \lambda \cdot \left({\frac{1}{{1 + e^{- \alpha^{k-1}_{i_{0}} - \alpha^{k-1}_{i1} \cdot \gamma \left[ {k - 1} \right]} }} - e\left[ k \right]} \right) \\ \alpha^{k}_{i_{1}} = \alpha^{k-1}_{i_{1}} - \lambda \cdot \left({\frac{1}{{1 + e^{- \alpha^{k-1}_{i_{0}} - \alpha^{k-1}_{i1} \cdot \gamma \left[ {k - 1} \right]} }} - e\left[ k \right]} \right)\! \cdot\! \gamma \left[ {t - 1} \right], \\ \end{array} \right. \end{aligned}} $$
where e[k] is, as in (7), an error indicator of the received packet at the instant k, which is obtained from the ACK/NACK report, and γ[t−1] is the estimated instantaneous SNR during the previous TTI t. Note that the values of \(\alpha ^{k}_{i_{0}}\) and \(\alpha ^{k}_{i_{1}}\) have to be updated simultaneously. Regarding λ, low values will lead to a slower but a more accurate convergence, while high values will accelerate the convergence, although it may also cause oscillations. However, it should be noticed that the logistic regression cost function is convex, so convergence is guaranteed.
eOLLA performance
Figure 5 shows the instantaneous offset value θ[ t] of the eOLLA for the same conditions as that in Fig. 4. Note that in this case, t=k since a transmission is carried out every TTI. In this figure, it can be seen that the convergence values are the same with that achieved for the traditional OLLA. In addition, instantaneous offset values have lower variance than the ones obtained in Fig. 4 for the same Δ down .
Throughput and aBLER results are shown in Table 4. For the eOLLA, an increment of Δ down does not lead to an important throughput degradation as in the case of the traditional OLLA (see Table 3). The throughput degradation when using the highest Δ down considered in these simulations is about 4 % for the eOLLA, while for the traditional OLLA, this degradation is doubled. In both cases, the aBLER T is met.
Table 4 System performance for eOLLA with different Δ down sizes
Results presented for the traditional OLLA and the eOLLA do not correspond to a realistic scenario, since an uncorrelated channel is assumed. However, they are useful in order to understand the dynamics of both the OLLA and the proposed eOLLA. In practice, AMC cannot be used in uncorrelated channels due to the outdating of CQI reports. Next, both OLLA and eOLLA have been evaluated in a correlated channel scenario. Furthermore, a bursty traffic pattern has been taken into account in order to give a complete picture.
Figure 6 shows the instantaneous offset values for both the traditional OLLA and the eOLLA for a correlated flat Rayleigh channel with a Doppler frequency (f D ) of 7 Hz and aBLER T =0.1, when a small Δ down step is used (0.001 dB). The transmission periods are expressed in percentage of the maximum traffic load case. First of all, notice that the same results are achieved by the eOLLA independently of the traffic load, since the eOLLA only needs the estimated iBLER (available at each TTI) to update the offset. In contrast, different performance is achieved by the traditional OLLA depending on the traffic load, since it needs an ACK/NACK report to update its state. As a consequence, when full buffer is assumed, i.e., t=k, both OLLA implementations have a similar performance; but as the traffic load decreases, it is harder for the traditional OLLA to follow the temporal channel variations since its offset is only updated when a CRC is received, thus degrading its performance compared to the eOLLA, which updates its offset every TTI.
Instantaneous offset comparison for a correlated flat Rayleigh channel (f D =7 Hz) with Δ down =0.001 dB
On the other hand, if the same simulation is carried out with a high step size Δ down =0.5, which ensures convergence, this convergence is easily achieved independently of the traffic load. However, low load is still a problem for the traditional OLLA since it is not able to properly follow the channel, as shown in Fig. 7.
Instantaneous offset comparison for a correlated flat Rayleigh channel (f D =7 Hz) with Δ down =0.5 dB
Table 5 summarizes the spectral efficiency results for different traffic loads and Δ down sizes for both the traditional OLLA and the eOLLA, assuming aBLER T =0.1 and a correlated flat Rayleigh channel with f D =7 Hz. First of all, note that for high Δ down sizes the performance of both OLLA techniques is degraded, independently of the traffic load. However, while this degradation does not exceed the 4 % for the eOLLA, in the case of the traditional OLLA, this degradation varies between 5 and 10 %. Furthermore, given a Δ down step size, the performance of the eOLLA does not vary with the traffic load. However, when using the traditional OLLA, low traffic load can degrade its performance with respect to the eOLLA up to 17 %. To sum up, the eOLLA outperforms the traditional OLLA while reducing the influence of the Δ down step size and the traffic load.
Table 5 Spectral efficiency (bps/Hz) comparison between traditional OLLA and eOLLA in correlated flat Rayleigh channel (f D =7 Hz)
eOLLA: application and simulation scenarios
The proposed eOLLA has been implemented in a complete 3GPP-LTE-A downlink simulator [10] in order to present realistic scenarios for which the eOLLA can significantly improve the performance of the traditional OLLA. These scenarios are based on LTE and LTE-A features. Both the traditional OLLA and the eOLLA have been evaluated for different sizes of Δ down , all of them ensuring convergence according to Subsection 2.1.
The 3GPP-LTE-A downlink simulator includes most of the features of physical (PHY) and medium access control (MAC) layers. Furthermore, it includes the LTE-A eICIC feature [16], which will be addressed in Subsection 4.5.
The MAC layer includes a hybrid automated repeat request (HARQ) process and an AMC process, which exchanges information with a channel aware scheduler. This scheduler allocates the LTE transport blocks (TBs) by assigning a set of physical resource blocks (PRBs) with a certain MCS [2] to each UE in order to meet some criteria.
At the BS, the PHY layer is made up of a coder system, which includes a CRC and a turbo coder; a quadrature amplitude modulation (QAM) mapper and an orthogonal frequency domain multiplexing (OFDM) modem. An AWGN channel and a multipath channel with temporal fading are included. At the UE, channel estimation and SINR estimation methods are implemented. With those methods, the channel state information (CSI) is reported to the BS [2]. The CSI is composed of the CQI, the precoding matrix indicator (PMI) and the rank indicator (RI). A QAM demapping and a channel-decoding process, which includes a CRC decoder and turbo decoder, is carried out to obtain the transmitted TBs. Finally, a HARQ entity manages the report of the ACK/NACK to the BS.
Table 6 summarizes the main simulation parameters. A typical pedestrian mobile speed (3 km/h) has been assumed to ensure the evaluation of the OLLA implementations under appropriate operating conditions of the AMC process. Regarding the channel aware scheduling, a round robin algorithm has been selected.
High traffic load with continuous transmission scenario
In this subsection, the performance of both the traditional OLLA and the eOLLA in a scenario with high traffic load is evaluated. In this scenario, both algorithms update their offsets continuously as there will always be queued packets to be transmitted. Then, the benefit of eOLLA comes from its ability to dynamically adapt the size of the step, thus reducing the offset variance.
Next, main results for BLER, throughput, goodput, mean packet delay and jitter [17] are shown for different sizes of Δ down . Except for the BLER, these results are expressed in percentage of the maximum value (which is also indicated in the figures). As shown in Fig. 8, there is no performance difference in terms of BLER since the aBLER T is met for both algorithms. However, it can be seen in Figs. 9 and 10 that a better performance in terms of throughput and goodput is achieved by the eOLLA for the majority of Δ down sizes. Only for the lowest Δ down sizes, throughput and goodput results are quite similar since the margin of adaptation of the step is very low. As the step size increases, the eOLLA gain is more remarkable, achieving a gain close to the 15 %.
BLER comparison for high traffic load with continuous transmission scenario
Throughput comparison for high traffic load with continuous transmission scenario
Goodput comparison for high traffic load with continuous transmission scenario
Finally, the performance in terms of delay is evaluated, which is a very useful metric [18]. Figures 11 and 12 show the results for mean packet delay and jitter, respectively. In this case, the eOLLA also outperforms the traditional OLLA. While for the traditional OLLA, the optimum size of Δ down is the lowest, the eOLLA tolerates higher sizes without a significant degradation of its performance.
Mean packet delay comparison for high traffic load with continuous transmission scenario
Jitter comparison for high traffic load with continuous transmission scenario
High load traffic with bursty transmission scenario
In some scenarios, it is possible that some UEs cannot be served even if they have queued packets and good channel conditions, because of low-priority assignment or in case the BS is serving a great number of UEs. In this situation, the UE may stay in idle mode during long periods; as a consequence, channel conditions may change considerably during the time interval between packets, so the offset of the traditional OLLA would be outdated (since it can only be updated when an ACK/NACK is received). However, as analyzed in previous section, the eOLLA is able to follow the channel variations even if no packets are received, since it uses the instantaneous SNR estimation to update its offset.
In this section, we evaluate a scenario where a data transmission is just allowed every 100 TTIs. Figures 13, 14, 15, 16, and 17 present the performance results in terms of throughput, goodput, mean packet delay, and jitter (expressed in percentage of the maximum value).
BLER comparison for high traffic load with bursty transmission scenario
Throughput comparison for high traffic load with bursty transmission scenario
Goodput comparison for high traffic load with bursty transmission scenario
Mean packet delay comparison for high traffic load with bursty transmission scenario
Jitter comparison for high traffic load with bursty transmission scenario
In Fig. 13, it can be seen that the traditional OLLA does not raise the aBLER T for the lowest values of Δ down , whereas the eOLLA achieves the aBLER T . The reason is that in the traditional OLLA, the offset cannot be updated continuously in order to follow the channel. As a result, Fig. 14 shows that for the lowest step sizes, the traditional OLLA decreases its throughput dramatically with respect to the eOLLA, as much as 15 %. Regarding the rest of performance metrics (Figs. 15, 16, and 17), the eOLLA generally outperforms the traditional OLLA while it is less influenced by the size of Δ down . Only for the lowest values of Δ down , delay metrics are pretty similar. However, since the aBLER T is not met for the traditional OLLA, these results are not meaningful.
Low-load traffic scenario
A M2M, online gaming [18] or an automated teller machine (ATM) scenario are examples of bursty traffic patterns with low data packet rates. In such scenarios, small data packets are exchanged over long periods of time; UE during these periods of time remain in idle mode. In LTE and LTE-A, there exists a minimum number of physical resources to allocate the TB to be transmitted [19]. For the DL, this number depends on the system bandwidth; for instance, for a system bandwidth of 5 MHz the minimum number of physical resources corresponds to one PRB. Thus, in case that the size of the packets to be transmitted is smaller than the minimum number of assignable physical resources, more redundancy will be included to fill them, i.e., the MCS will be modified making it more robust than the proposed by the reported CQI. As a consequence, less erroneous TBs will be received, and therefore, the aBLER will be lower than the aBLER T , making the traditional OLLA to increase its offset in order to meet the target aBLER. Note that in this case, the traditional OLLA is unnecessarily forcing the aBLER to meet the target, since increasing the robustness of the MCS does not mean saving physical resources when the minimum number of assignable PRBs is used. Instead, it will cause erroneously received TB that could be avoided without increasing the amount of physical resources. Therefore, if the eOLLA is used in this scenario, since its offset is not affected by the number or erroneous TBs received but by the instantaneous channel state, it will not try to force the aBLER T to be met.
Next, the figures show the performance of the traditional OLLA and the eOLLA for an online gaming scenario [20], whose mean packet size is 70 bytes and mean period between packets is 50 ms. Figure 18 shows that the average BLER is not achieved for the eOLLA whereas the traditional OLLA achieves it for the majority of the Δ down sizes. Regarding the normalized throughput, 100 % is always achieved since the capacity of the system is higher than the load of the traffic source. This fact also means that packet delay is always the minimum achievable value, i.e., 1 ms. Finally, goodput results of Fig. 19 show how the eOLLA outperforms significantly the traditional OLLA without any extra cost in terms of physical resources usage.
BLER comparison for low load traffic scenario
Goodput comparison for low load traffic scenario
eICIC scenario
In LTE-A, the time domain-enhanced inter-cell interference coordination (eICIC) is defined as a technique to manage interference in heterogeneous networks (HetNets). An extensive description of this technique can be found in [16]. Briefly, in a scenario composed by a macro- and a pico-cell, a bias is applied to the coverage area of the pico-cell to balance the number of users associated to both types of cells. This process is named cell range expansion (CRE). Pico-cell users (PUEs) that are located in this CRE area generally suffer from very high interference from the macro-cell, since its transmission power is higher than the transmission power of the pico-cell. Thus, in order to improve the performance of these users, the macro-cell periodically does not schedule any transmission to their associated macro-cell users (MUEs), even if they have queued packets, generating the so called almost-blank subframes (ABSs). During the transmission of these ABSs, the pico-cell prioritizes the transmission to the PUEs of the CRE area since their SINR conditions improve significantly. On the other hand, during the transmission of normal subframes from the macro-cell, CRE PUEs are not scheduled by the pico-cell.
Thus, the eICIC technique implies that MUEs and CRE PUEs are not scheduled over certain periods of time (see Fig. 20). Therefore, in a similar way as the scenario described in Subsection 4.3, the offset of the traditional OLLA may not be able to follow the channel variations during these periods, while the eOLLA is.
Effects of ABSs in normal PUEs
Next, a performance comparison for both the traditional OLLA and the eOLLA is presented for a MUE and 30 ABSs transmitted every 40 TTIs. Note that this scenario is a mix of the two previously evaluated high-load traffic scenarios, since there is no continuous transmission, but the interval between transmissions is much lower than in the case of the high-load traffic with a bursty transmission scenario. Firstly, in Fig. 21, BLER results are presented for both algorithms, showing that the aBLER T is met in both cases. However, for the lowest size of Δ down , the eOLLA is closer to the target than the traditional OLLA (which is consistent with results of subsection 4.3). Furthermore, in Figs. 22, 23, 24, and 25, it is shown how the proposed eOLLA also outperforms the traditional OLLA in terms of throughput, goodput, delay, and jitter, as much as a 10 %.
BLER comparison for eICIC scenario
Throughput comparison for eICIC scenario
Goodput comparison for eICIC scenario
Mean packet delay comparison for eICIC scenario
Jitter comparison for eICIC scenario
A summary of the maximum gain of the proposed eOLLA with respect to the traditional OLLA for the different presented scenarios is shown in Table 7.
Table 7 Summary of eOLLA maximum gain with respect to the traditional OLLA
To sum up, the eOLLA outperforms the traditional OLLA in the whole set of evaluated scenarios. Then, for high-load traffic sources with continuous transmission, the ability of the eOLLA to adapt its step size is the cause of outperforming the traditional OLLA for the highest Δ down . Regarding the bursty traffic sources, the ability of the eOLLA to follow the channel variations even if no packet is received is the key factor. Finally, for the eICIC scenario, a combination of both abilities are exploited. Regarding the size of Δ down , previous results show that while for the traditional OLLA the selection of an appropriate value (which is different depending on the scenario) is crucial, for the eOLLA, there is a wide range of values that have a similar performance.
In this paper, the AMC process of LTE and LTE-A has been modeled in order to carry out a complete analysis of the well-known traditional OLLA algorithm. In particular, we present a model of the instantaneous BLER and the averaged BLER by means of binary logistic functions and modified binary logistic functions, respectively. Then, an expression to obtain the maximum step size of the traditional OLLA that ensures convergence has been presented. Furthermore, it has been studied how different sizes of this step lead to different performances depending on the specific scenario evaluated, even if they guarantee convergence. Next, a new approach to this algorithm, the enhanced OLLA (eOLLA), has been proposed based on logistic regression. This new algorithm outperforms the traditional OLLA since it is able to (1) dynamically adapt its step size according to the channel state and (2) update its offset value independently of whether a data packet has been received or not. Finally, a comparison of the performance of both algorithms has been carried out under different LTE and LTE-A scenarios, showing that the proposed eOLLA outperforms the traditional OLLA in all the cases.
ST Chung, AJ Goldsmith, Degrees of freedom in adaptive modulation: a unified view. IEEE Trans. Commun. 49(9), 1561–1571 (2001). doi:10.1109/26.950343.
3GPP TS 36.211, Evolved universal terrestrial radio access (E-UTRA); Physical Channels and Modulation (Release 10), V10.7.0, (Sophia Antipolis Valbonne, France, 2013). http://www.3gpp.org/ftp/Specs/archive/36_series/36.211/36211-a70.zip.
A Sampath, P Sarath Kumar, JM Holtzman, in IEEE Vehicular Technology Conference, vol. 2. On setting reverse link target sir in a cdma system, (1997), pp. 929–9332. doi:10.1109/VETEC.1997.600465.
P Song, S Jin, in International Conference on Communications and Information Technology (ICCIT). Performance evaluation on dynamic dual layer beamforming transmission in tdd lte system, (2013), pp. 269–274. doi:10.1109/ICCITechnology.2013.6579562.
KI Pedersen, G Monghal, IZ Kovacs, TE Kolding, A Pokhariyal, F Frederiksen, et al, in IEEE Vehicular Technology Conference (VTC Fall), Baltimore, USA. Frequency domain scheduling for OFDMA with limited and noisy channel feedback, (2007), pp. 1792–1796, doi:10.1109/VETECF.2007.378.
MG Sarret, D Catania, F Frederiksen, AF Cattoni, G Berardinelli, P Mogensen, in 21th European Wireless Conference, Budapest, Hungary. Dynamic outer loop link adaptation for the 5G centimeter-wave concept, (2015), pp. 1–6.
A Duran, M Toril, F Ruiz, A Mendo, Self-optimization algorithm for outer loop link adaptation in lte. IEEE Commun. Lett. 19(11), 2005–2008 (2015). doi:10.1109/LCOMM.2015.2477084.
J Ortin, P Garcia, F Gutierrez, A Valdovinos, Performance analysis of turbo decoding algorithms in wireless ofdm systems. IEEE Trans. Consum. Electron. 55(3), 1149–1154 (2009). doi:10.1109/TCE.2009.5277969.
A Agresti, Categorical Data Analysis (John Wiley and Sons, Hoboken, New Jersey, 1990).
G Gómez, D Morales-Jiménez, JJ Sánchez-Sánchez, JT Entrambasaguas, A next generation wireless simulator based on MIMO-OFDM: LTE case study. EURASIP J. Wirel. Commun. Netw. 2010:, 15–11512 (2010).
3GPP TS 36.212, Evolved universal terrestrial radio access (E-UTRA); Multiplexing and Channel Coding (Release 10), V10.9.0, (Sophia Antipolis Valbonne, France, 2015). http://www.3gpp.org/ftp/Specs/archive/36_series/36.212/36212-a90.zip.
S Banach, Sur les operations dans les ensembles abstraits et leur application aux equations integrales. PhD thesis (University of Lwow, Poland (now Ukraine), 1920).
3GPP TS 36.304, Evolved universal terrestrial radio access (E-UTRA); User Equipment (UE) Procedures in Idle Mode (Release 10), V10.8.0, (Sophia Antipolis Valbonne, France, 2014). http://www.3gpp.org/ftp/Specs/archive/36_series/36.304/36304-a80.zip.
RU Andrzej Cichocki, Neural Networks for Optimization and Signal Processing (Wiley, Chichester, 1993).
A El-Koka, K-H Cha, D-K Kang, in International Conference on Advanced Communication Technology (ICACT), PyeongChang, South Korea. Regularization Parameter Tuning Optimization Approach in Logistic Regression, (2013), pp. 13–18.
KI Pedersen, Y Wang, S Strzyz, F Frederiksen, Enhanced inter-cell interference coordination in co-channel multi-layer LTE-advanced networks. Wireless Commun. 20(3), 120–127 (2013). doi:10.1109/MWC.2013.6549291.
H Schulzrinne, S Casner, R Frederick, V Jacobson, RTP: a Transport Protocol for Real-time Applications, IETF RFC 3550 (2013). https://www.ietf.org/rfc/rfc3550.txt.
IM Delgado-Luque, F Blanquez-Casado, MG Fuertes, G Gomez, MC Aguayo-Torres, JT Entrambasaguas, et al, in European Signal Processing Conference (EUSIPCO), Bucharest, Romania. Evaluation of latency-aware scheduling techniques for M2M traffic over LTE, (2012), pp. 989–993.
3GPP TS 36.213, Evolved universal terrestrial radio access (E-UTRA); Physical Layer Procedures (Release 10), V10.13.0, (Sophia Antipolis Valbonne, France, 2015). http://www.3gpp.org/ftp/Specs/archive/36_series/36.213/36213-ad0.zip.
D Drajic, S Krco, I Tomic, P Svoboda, M Popovic, N Nikaein, N Zeljkovic, in Conference on Wireless On-demand Network Systems and Services (WONS). Traffic generation application for simulating online games and m2m applications via wireless networks, (2012), pp. 167–174, doi:10.1109/WONS.2012.6152224.
S Coleri, M Ergen, A Puri, A Bahai, Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Trans. Broadcast. 48(3), 223–229 (2002). doi:10.1109/TBC.2002.804034.
This work has been partially supported by the Spanish Government and FEDER (TEC2013-44442-P).
Department of Communications Engineering, Universidad de Málaga, Malaga, Spain
Francisco Blanquez-Casado
, Gerardo Gomez
, Maria del Carmen Aguayo-Torres
& Jose Tomas Entrambasaguas
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Correspondence to Francisco Blanquez-Casado.
Blanquez-Casado, F., Gomez, G., Aguayo-Torres, M.d. et al. eOLLA: an enhanced outer loop link adaptation for cellular networks. J Wireless Com Network 2016, 20 (2016) doi:10.1186/s13638-016-0518-3
Link adaptation
BLER


text:
Video Shakkei
Kreider, Kristen and O'Leary, James. 2010. Video Shakkei. In: "Video Shakkei, Osaka - Nara - Kyoto - Ise, Japan - June-July 2010", Osaka - Nara - Kyoto - Ise, Japan. [Performance]
Kreider, Kristen and O'Leary, James
Abstract or Description:
'Partial control is exercised through the use of the frame. Each frame, each part of a sequence qualifies, reinforces, or alters the parts that precede and follow it. The associations so formed allow for a plurality of interpretations rather than a singular fact.'
– Bernard Tschumi, Architecture and Disjunction
Performance No. 1 takes place at Umeda Sky Building (梅田スカイビル Umeda Sukai Biru) in Osaka, designed by architect Hiroshi Hara. Hara's building has been tugging on the imagination for some time as an icon of dynamic, punctured, post-everything sequence of spaces. This is the new urbanism: both post and pre-apocalyptic, reminiscent of older things, but pointing ahead. Ahead, in this case, is very much upwards.
The site for Performance no. 2 is Shingonshu Honpukuji (真言宗本福寺水御堂), also known as the Water Temple, designed by architect Tadao Ando and located on Awaji Island in Japan's Inland Sea. Associated with an existing Temple of the Shingon Sect, this new addition to the Temple precinct is ordered by a dynamically unfolding sequence of spaces – a series of spatial contractions and releases – to calibrate the approach to the new building, as one rises up the hill. The final bodily rotation reveals the dramatic opening out of the elliptical pond of water lilies, which one descends through to enter the temple proper. The experience is of immersion in a concrete cave of vermilion and gold. Performance No. 2 was conducted in the penultimate ascending space before the water reveals itself.
Performance No. 3 takes place in the linear space that links eastern and western precincts of Hōryū-ji (法隆寺 lit. Temple of the Flourishing Law) – a Buddhist temple located in Ikaruga famous for having some of the oldest extant wooden structures in the world. The act we perform is about communication, intuition and the limits if vision. We feel as if we are moving very slowly forward with our eyes wide open, but realise that we are in complete darkness. We scan the horizon for any distinction between ground and sky, so that we can orient ourselves, but this distinction continues to evade revelation.
Performance No. 4 is sited in the Origin I Building in Kyoto, designed by architect Shin Takamatsu. The actions explore the space of the central threshold, a space between ground and sky. The architectural metaphors deployed by Takamatsu are mechanistic and, frankly, sexual – moving from phallus to womb. Entry through the facade feels like you have been employed as an extra in an architectural porn movie, and one leaves feeling slightly kinky, used and yet energized. The performance explores layering, reflectivity and mirroring effects on either side of the main doorway, which, as it moves between open and closed conditions – generates multiple super-positioned images of male and female protagonists.
We arrive at the Museum of Traditional Crafts (京都伝統産業ふれあい館) in Kyoto, designed by architect I.M. Pei, as the sky bellies up for its next big squall. People scurry across the courtyard, safe beneath the personal architecture of umbrellas. This piece becomes about synchronicity: our movements blind and in tune, choreographed with the design of the courtyard space. Five times around the tree – forward, then backward – meets the slow promenade of our protagonist in the rain. Our movements stop just as the lights go on, illuminating puddles and signalling 'the end'.
Performance No. 6 takes place at an intersection in Kyoto on the corner of Niomon-dori and Jingu-michi Street, in front of the Tori of the Heian Shrine (平安神宮 Heian jingū) and the Museum of Modern Art, Kyoto (京都国立近代美術館 Kyōto Kokuritsu Kindai Bijutsukan). We enact this sequence of events in the pouring rain. Umbrella's approach in waves, and progress orthogonally – N-S-E-W – as we do, recording rainfall, droplets gathering, the mask of the opaque surface, the changing of the chromatic range between red and green.
Performance No. 7 at the monumental gate of the Higashi Hongan-ji (東本願寺 Higashi Hongan-ji), or, the Eastern Temple of the Original Vow, is imbued with a nocturnal state of closed-ness to the world. We try to engage with the strange force of illumination, which renders the gates visible at night, and also paints everything in a silvery monochrome strangeness. We communicate, and subsequently fail to communicate. We are realizing that this series of performances is primarily about harmonic communication between bodies and spaces. Sometimes messages are mis-communicated, which leads to the unforeseen, and unimaginable.
Performance No. 8 is sited at Ryōan-ji (Shinjitai: 竜安寺, Kyūjitai: 龍安寺, The Temple of the Peaceful Dragon) in Kyoto. The wall bounding the Ryoan ji Temple karesansui garden is a beautiful thing. This surface, disguised as garden, is as plastic as the mind, and equally as dumb. The landscape oscillates between figurative and abstract, as does thought.
The Sagano Bamboo Forest (嵯峨野竹林) in Arashiyama, western Kyoto, is dense, sonorous and easily as vertical in emphasis as a cathedral. The sounds, the smells and possibly the mosquitoes inspired the actions in this piece. The piece is short, like an explosion. The locals laugh. We do too.
Performance No. 11 takes place at Meoto Iwa (夫婦岩), the 'Wedded Rocks,' in Futaminaura on the Ise peninsula. We arrive at dawn on a day of clouds. The famous sunrise is denied us, hidden behind a dense wall of Pacific grey. We want to become the sun, to enliven with light. Frustrated with spectatorship, we want to engage physically with the entwined rocks, but this is sacred ground, and we are unsure of the protocols. Instead, we skirt along the edge – tangential.
Performance No. 12 is sited at Gekū (外宮), the outer shrine of Ise Shrine (伊勢神宮 Ise-jingū) on the Ise peninsula and twinned site with Naikū (内宮) (see below). As with Naikū, the buildings here are constructed in pairs and completely renewed every twenty years in accordance with ancient tradition. We choose a location outside of the gates to perform, situate ourselves and become still. We breathe. We are rocks.
The peace, reverence and ritual of Naikū (内宮), the inner shrine of Ise Shrine (伊勢神宮 Ise-jingū) on the Ise peninsula – a cacophony of wildlife, within which humans barely register. The buildings in this precinct are constructed in pairs. In turns, the buildings are completely renewed every twenty years in accordance with ancient tradition. Every entity on the site is considered sacred including all flora and fauna. We stop. We concentrate on stillness. We endeavour to become part of the site, for however short a time.
It is our last night in Japan and we are at Kansai Airport (関西国際空港 Kansai Kokusai Kūkō) in Osaka, designed by architect Renzo Piano. At dawn we will depart, but there is one more performance to do. We take the bus to the observation hall overlooking the runway. The lights are glorious; the moon is full. Music plays from the speakers and we finish up our trip to Japan with a dance in the night under a Kansai sky …
http://www.kreider-oleary.net/works/#/video-shakke...
14 site-specific performances in Kansai region of Japan and video documentation included as part of installation work Video Shakkei: Space, Performance, Drawing at The Centre for Drawing (London, UK, 2009).
Video Shakkei, Kreider + O'Leary, Kreider, O'Leary
Osaka - Nara - Kyoto - Ise, Japan

redpajama_set_name: RedPajamaCommonCrawl


Layout takes advantage of the slight southern facing slope of the parcel, while varied east to west spacing and backtracking reduces inter-row shading on the rolling terrain.
The site is surrounded on all sides by towering eighty foot pine trees. We used 3D modeling to capture the shadows cast upon the uneven terrain to ideally place the system within the site.
The 1500V system design enabled longer DC runs and the ability to group inverters for electrical installation efficiency.
This in the first solar installation in the town of Bedford,VA!
SEI Engineering provided the full electrical design (PV system, auxiliary power, medium voltage design) and the structural design (inverter group structures, equipment pads). SEI Engineering also served as the Design Project Manager tasked with coordinating all contractors involved in the design phase, including civil, surveyor, and equipment manufacturers.


text:
Q: clearInterval not working in React Timer App I've been working on improving my React skills by creating a pomodoro timer app. For some reason I can't seem to get clearInterval working:
startTimer() {
    const { started } = this.state;
    var timer;
    if (!started) {
      timer = setInterval(this.countdown, 1000);
      this.setState({ started: true });
    }
    else {
      clearInterval(timer);
      this.setState({ started: false });
    }
  }

No console errors. Can confirm that it's definitely running that part of the conditional statement (when I log this.state.started it shows false). The timer just keeps ticking and doesn't actually stop.
Rest of the code:
import React, { Component } from 'react';

class Timer extends Component {
  constructor(props) {
    super(props);
    this.state = {
      secs: 60,
      started: false
    };
    this.startTimer = this.startTimer.bind(this);
    this.countdown = this.countdown.bind(this);
  }

  countdown() {
    this.setState({ secs: this.state.secs - 1});
  }

  startTimer() {
    const { started } = this.state;
    var timer;
    if (!started) {
      timer = setInterval(this.countdown, 1000);
      this.setState({ started: true });
    }
    else {
      clearInterval(timer);
      this.setState({ started: false });
    }
  }

  render() {
    const { secs } = this.state;
    console.log('secs:', secs)
    console.log('started:', this.state.started);
    return (

Session

{this.props.session}:{secs == 60 ? '00': secs}

    );
  }
}

export default Timer;

A: Initialize this.timer in the constructor. Then create the setInterval and assign it to this.timer, which will allow you to clear it later.
import React, { Component } from 'react';

class Timer extends Component {
  constructor(props) {
    super(props);
    this.state = {
      secs: 60,
      started: false
    };
    this.timer = null;
    this.startTimer = this.startTimer.bind(this);
    this.countdown = this.countdown.bind(this);
  }

  countdown() {
    this.setState({ secs: this.state.secs - 1});
  }

  startTimer() {
    const { started } = this.state;
    if (!started) {
      this.timer = setInterval(this.countdown, 1000);
      this.setState({ started: true });
    }
    else {
      clearInterval(this.timer);
      this.setState({ started: false });
    }
  }

  render() {
    const { secs } = this.state;
    console.log('secs:', secs)
    console.log('started:', this.state.started);
    return (

Session

{this.props.session}:{secs == 60 ? '00': secs}

    );
  }
}

export default Timer;

A: if you aren't trying to create a class
 if(condition_is_true){
   const checkUntilConditionIsFalse = setInterval(() => {
    if (condition_is_false) {
      clearInterval(checkUntilConditionIsFalse);
    }
  }, 1000);
}

redpajama_set_name: RedPajamaStackExchange


text:
15 Minute History
15 Minute History is a history podcast designed for historians, enthusiasts, and newbies alike.
Episode 5: Mapping Perspectives of the Mexican-American War
https://podcasts.la.utexas.edu/15-minute-history/wp-content/uploads/sites/13/2018/03/Episode-5_-Mapping-Perspectives-of-the-Mexican-American-War.mp3
This episode looks at US perceptions of Mexico through map making during the US / Mexico War, in which a private publisher sold maps that were reissued annually to reflect ongoing progress in the campaign. Intended for a general, popular audience, these maps served as propaganda in aid of the conflict, but historians and military analysts alike have ignored them until recently—even though they may well have influenced the positioning of the border at the war's end.
Guest Chloe Ireton looks at the intriguing history of maps as propaganda and the role of two publishing houses—J. Disturnell and Ensigns & Thayer—not only in rewriting the history of the Mexican-American war, but in influencing the outcome of the war even as it was still ongoing.
Chloe IretonLecturer, University College, London
Christopher RosePostdoctoral Fellow, Institute for Historical Studies, The University of Texas at Austin
Read Transcript...
Specifically, I am going to talk about maps of Mexico produced by two US publishing houses based in New York: J. Disturnell and Ensigns and Thayer, which each organization revised and republished on numerous occasions during the war years.
J. Disturnell was one of the most prolific map publishers in the US during the mid nineteenth century. The publisher focused mostly on tourist guides and maps of North American states and regions. Disturnell published at least three separate maps of Mexico between 1846 and 1848, each of which he revised and republished almost annually during and in the aftermath of the war years.
J. Disturnell's 1847 Map of Mexico (click on map for a navigable/zoomable version)
A defining feature of Disturnell's Mexico maps was that he copied the cartography from older map publications, but added annotations and inserted other notes and sketches, which served to visually represented North American gains during the war. This process of adding details as they happened and republishing, illustrated the new literal and figurative definitions of the American nation almost in real time to his audience.?¹
In 1847 Disturnell published the Mapa de los Estados Unidos de Mejico, Segun lo organizado y definido por varias actas de Congreso de dicha Republica: y construida por las mejores autoridades.?² It was a copy of a map printed a decade earlier in Paris called Congreso General Mejicano. Mapa de los Estados Unidos Mejicanos, 1837. Disturnell's map is bilingual, retaining the Spanish title and therefore also the ironic notion that the Mexican Republican Congress had approved the contents of his map. In fact, his map was a copy of a map that was twenty years out of date.?³ The English part of the map on the most part represents Disturnell's descriptive insertions of the geography and history of Mexico and recent events, while the Spanish text represents the areas of the map that he directly copied from the Paris edition.
What is distinctive about the Disturnell Maps?
Disturnell inserted key symbology into his 1847 map and the 1850 revised edition of the map in order to strengthen a sense of US national identity. The most obvious purpose of Disturnell's maps was to redraw the boundaries of the US nation. When comparing Disturnell's 1847 map and the original 1837 version that he copied, it is clear that Disturnell altered the border.
Congreso General Mejicano. Mapa de los Estados Unidos Mejicanos, 1837. (click on map for high resolution version).
In the 1837 version, Texas was represented as part of Mexico and therefore not independent, even though Texas had declared itself a republic in 1836. In the 1847 copy, Disturnell altered this, portraying Texas as independent from Mexico. As a result, Disturnell changed the US-Mexican national boundary too; clearly drawing the border between the two nations as running across the Rio Grande.
It is important to remember that this was an anticipatory move – in 1847 neither side had reached a formal agreement about the border. It was not until the Treaty of Guadalupe Hidalgo in 1848 that both sides finally agreed that the course of the Rio Grande would define the new national boundaries.?4
Aware of the fact that the border had not yet been formally agreed, Disturnell inserted the text, Boundary As Claimed by the United States. In spite of this text, his portrayal of the "new" border would have made the contentious border a reality in the minds of viewers of the map, since the border is clearly drawn at the Rio Grande.
As an example of the power of maps and the boundaries that they portray, in a fundamental study on the politics involved in mapping the US-Mexico border after the war, Paula Rebert, has illustrated how the political negotiators in both nations during the 1848 border treaty negotiations mistakenly used the seventh edition of Disturnell's highly inaccurate 1847 map.?5 Rebert argued that this caused severe conflicts between the cartographers from both nations, as the cartographical representations did not reflect the geographical realities on the ground, thereby causing difficulties in plotting the new border.
When taking into account the power of visual representations, it is worth asking whether Disturnell's anticipatory drawing of the new border, which in effect made a reality of something that had not yet been realized in a treaty, affected the political choice of where the border would run when politicians did actually negotiate the treaty. Such questions tend to lend to historical speculation, as it is impossible to discern the precise effect that Disturnell's map may or may not have had during the negotiations of the Treaty.
So you spoke of how Disturnell's maps created a new sense of national identity, except for redrawing the boundary what else did he change?
One of the salient features of both Disturnell's 1847 and 1850 editions was the pressing need to create a revised national sense of identity that takes into account impending extended geographical boundaries. Both editions feature heavy annotations of the new territories. In 1847 Disturnell noted that these regions were "as claimed by the United States." Reflecting current events, the 1850 map represents the Hidalgo treaty of 1848 and California is clearly outlined as a US territory. In the 1847 edition, Disturnell mapped a California that Mexicans had failed to properly inhabit and put to productive use. There are ample descriptions of how Native Americans dominated the territories and of independent Indian tribes that both the Mexicans and their Spanish predecessors had continuously failed to assimilate into the Mexican nation.
In contrast, the textual inserts in California in the 1850 edition, present a bountiful ex-Mexico with vast natural resources that would be beneficial for the US. For example, whereas in 1847 edition Salt Lake just appeared as a name, in the 1850 edition Disturnell inserted a text describing how important the region was: "the Salt Lake is one of the wonders of nature, and perhaps without a rival in the world, is a saturated solution of salt, of an hundred miles of diameter". In another part of California Disturnell inserted, "The bear river valley with its rich bottom, fine grass, fields of blue flax, hot mined springs, volcanic rocks, saline efflorescent is also a rich and interesting region."
What do these differences tell us? The differences between the two editions illustrates that in 1847 Disturnell was creating a narrative of Mexican failure to inhabit the lands and control the Indian tribes in the region. Through the descriptions in the 1850 edition of the vast resources that the US had discovered, Disturnell was implicitly suggesting that the US had a rightful claim to these regions because Mexico had failed to inhabit the lands, integrate indiand within the nation state, and most importantly establish any productive industry in the area.
Are these themes of Mexico's failure and the US's right to the new lands present in other maps of the period too, or are the Disturnell maps unique in this regard?
Absolutely, there are certainly other examples. For example, An Ornamental Map of the United States and Mexico, designed by W. Phelps and published by Ensigns and Thayer also presents similar narratives on the new territories. The map was revised at least once, since there are two surviving copies from 1846 and 1847. Like the Disturnell map, the Ornamental series are visual representations of the new and extended nation rather than accurate representations of the geography. The Ornamental map is more visually descriptive, since the actual cartographic representations take up approximately half of the total image.
Therefore, while Disturnell's maps could have been confused for a scientific and accurate representation of cartography, these Ornamental Wall Maps served a clearer purpose of illustrating key aspects of US geography and history to a US audience. In spite of the immediate visual differences between Disturnell's and Ensigns and Thayer's maps, both represent similar national narratives to Disturnell's descriptions of California in 1850.
Description of New Mexico and the Californias from Ensigns and Thayer's 1846 Ornamental Wall Map
The insets in the Ornamental Map continue the narrative of presenting Mexico as a failed state and show US possession of the new territories before both nations had reached any agreements. An inset describing the new territories describes New Mexico and California's natural resources and viability for economic development within the US nation. The last line in the description of New Mexico and California reads "These countries are destined to become important acquisitions to our REPUBLIC." Therefore, Ensigns and Thayer, like Disturnell, were also defining new American territories in 1846 and 1847, prior to it being a reality.
Other insets describing Mexican history, also serve to justify such acquisitions by the US, by representing Mexico as a failed state. The insets on Mexican history, feature an in depth description of the wonders of the Aztecs and Montezuma, with no description whatsoever of present day Mexico except to say that the Mexicans have not preserved these historical treasures. In this narrative, Mexico had therefore destroyed the legacy of a great civilization, and failed to become a functioning nation state, since there was nothing worthy of describing in the present day country. As such, this map presents Mexico as a defunct empire, thereby justifying US colonialism of it.
The representation of Mexico as a failed state is further justified through the presentation of statistical data. These statistical tables are really striking and would make great teaching resources. By using different sets of data to compare the two nations, Ensigns and Thayer represented Mexico as having the potential to be economically productive because of its natural resources, but instead being highly financially indebted because of the country's inability to establish productive industries. This statistical visual representation therefore served to justify US supremacy.
For example, the US table lists the "Amount of Continental Money Issued," at $358,000,000, whereas for Mexico the table provides information about the level of public debt, $16,000,000 and the revenue from the mining industries, $20,000,000. The details of the revenue from US industries however, were not provided. Instead, these mapmakers carefully tailored the statistics to make the US look far more prosperous and dominant, while portraying Mexico as a desirable place for US investment and colonial expansion, especially since it was so incapable of managing its own financial affairs.
The data also served to represent Mexico as inferior, in terms of progress. For example the supposed number of Indians in the United States is completely insignificant, 744,685 in contrast 17,063,355 total population, whereas in Mexico almost fifty percent of Mexico's 'meagre' population of 8,000,000 were supposedly Native Americans. The two timelines of events presented in the table are also indicative of these narratives. The historical timeline of the US represents events such as:
First Colonial Congress
First Union Convention
Constitution Adopted
Peace Concluded
Mexico Invaded
In contrast, the representation of Mexican history is marred with struggles to establish a nation. Key events described include:
Iturbide, a military chief declared Emperor
Divided into 14 States, 3 territories and one district
State Legislature Abolished
Santa Ana Declared Dictator
Texas and Other States Rebel.
These competing visualization of history, the choice of the events described and the language utilized all represented clear US superiority as a nation that had successfully fought wars and emerged as a progressive nation with a rule of law. In contrast, the data illustrated Mexico as ripe for conquest. According to the timeline, Mexico had been marred with political upheavals and failed attempts to establish a nation state.
Lastly, the statistics also represented Mexico as a region that had failed to shake off its Catholic colonial history. In spite of the significant anti-clerical conflicts and debates that were ongoing in Mexico at the time, the statistics table for Mexico states that the country had 10,000 priests.?6 No mention of religiosity in the US is made in the respective table.
Representing Mexican Catholicism in cartography is not unique to this map. In Disturnell's 1850 edition of the national map of Mexico, he divided the various states by religious administration, thereby also representing an image of Mexico as fervently Catholic. These numeric and visual representations failed to illustrate the acute internal battles in Mexico over the power of the clergy, and instead drew a picture of continuity from colonial rule under the Spanish Catholics, symbolizing a clear lack of progress since Independence.
You mentioned that these maps were often republished regularly throughout the war. Do we get any sense of the ongoing war through the maps?
Yes, absolutely! Disturnell permeated textual representations of US military activities throughout the 1847 national map of Mexico. Routes that US troops established into Mexican cities and the locales where key battles took place are represented by thick red lines with texts such as, General Woods' 1846 Route from San Antonio to Monclova. Another route called General Taylor's route of 1846 visually represents the route from Corpus Christi Bay to Puerto Isabel, where the text informs the viewers of the battles of Palo Alto and Rosaca de la Palma in 1846 and the establishment of the US military base Fort Brown. The route then continues south of the Rio Grande to Mier and then South West through Monterrey to Saltillo.
Disturnell does not provide information regarding the subsequent battles that took place in those towns, nor whether the troops were still there when the map was published. The information simply represented that the Generals had established the routes. Through the marking of these routes, Disturnell was informing a US audience of the most recent events in the war.
Disturnell's "Seat of War" Map. (Click on map for navigable/zoomable version).
In the same year J. Disturnell published a map called 1847 called Pocket Map: A Seat of War in Mexico, Being a copy of General Arista's Map, taken at Resaca de la Palma, with additions and Corrections; Embellished with Diagrams of the Battles of 8th & 9th May and Capture of Monterrey with a memorandum of forces engaged results &c,. and Plan of Vera Cruz and Castle of San Juan de Ulua. The Disturnell Seat of War map illustrates the most recent events in the War in almost "real-time." The map was a copy of a Mexican map that General Taylor discovered among the possessions of General Bautista after the US victory at Resaca de la Palma in 1846.
This discovery had been of huge military importance to the Americans since the map illustrated accurate routes in northern Mexico. Previously US generals had relied on highly inaccurate maps that did not have detailed information about the terrains to the south of the Rio Grande. Military historians have argued that this map was of key importance in the decisive victories along the route to Saltillo.?7 General Taylor appropriated the map in the battle of 1846 and by 1847 Disturnell had copied and printed his version of it.
The red routes in the Seat of War map are far more extensive than those represented in Disturnell's 1847 national map. Therefore, if these maps are taken at face value, in one year alone the extent of US penetration into Mexico had almost doubled. Disturnell also inserted red flags to represent towns taken by Americans – a feature that was not present in his previous 1847 map. These changes illustrate why the New York map printers revised the editions and republished maps so frequently. The fact that the same publisher printed two maps in the same year that represented such different visions, suggests that maps were in fact a means of visually representing current events in real time to a North American audience.
The last significant mode of representing recent events that Disturnell employed was the inclusion of insets depicting the major battlefields. The 1847 national map had two insets, while the 1850 had five and the 1847 Seat of War map had six. These map insets are key additions to the map as they illustrate the extent of US military penetration deep into Mexican territory. Most often, these insets represented events that had occurred less than a year before the publication of the map.
These images depicted US victories and clear military superiority and continued the narrative of Mexico as a failed state. An inset that is present in all of the maps is a sketch drawn by J. H. Eaton 3rd that represented the US victories in Palo Alto de la Cruz and Besala de la Palma along the Rio Grande on the 8th and 9th of May 1846.
The second inset in the 1847 map, is a sketch of the Gulf of Mexico drawn by Admiral V. Admiral Baudin. It represents the bay of Veracruz and the roads from Veracruz city. The map shows key US losses, such as the sinking of the Brigg Sommers boat in crossfire in the bay on Dec 13th 1846.
In the 1850 edition of the national map, Disturnell featured a diagram of the battleground on February 22nd and 23rd of 1847 in Saltillo explicitly showing the positioning of US and Mexican troops. Another inset in the 1850 map of Monterrey and its environs clearly illustrates that Mexicans were defending a very small fraction of the town when the US took possession of it. When you look at the inset, the position of the Mexicans is illustrated through shading, thereby making it clear to the viewer that the Mexicans had little power.
Disturnell's Seat of War map includes further references to US victorious imagery. In a textual insert Disturnell described how, although vastly outnumbered by their Mexican enemies in every single battle, the US still won and had relatively few casualties. The Mexicans on the other hand always suffered vast losses. Such textual representations are complemented by an inset image inserted West of Mexico of a US soldier on a horse carrying a large American flag who is devastating Mexican soldiers with his superior force. In the image, two Mexicans lay slain on the ground by the horse's feet. Such imagery would have served to evoke national pride and assert an imagery of US dominance over Mexico.
These varied visual depiction of US victories and the lack of any representation of US losses would have implied to a US audience that the Mexicans did not seriously resist or defend their nation. Therefore, the insets continued the narrative of Mexico as a defunct and failed nation state that were implicit in the symbolism of the textual inserts and insets in other parts of Disturnell's national maps and the Ornamental Wall maps.
So how would you sum up these US maps?
The American maps published during the war years often represented a direct need to visualize the rapidly changing geography of the American nation and explain the victories of the war, as well as justify the need for the war. Imagery illustrated the prowess of the US military as well as the many victories.
The representation of Mexico served as a means of describing the "Other" in order to self-define as a nation. Comparing US natural resources, economy, population, and other key factors to those of Mexico by using different types of data and imagery meant that cartographers were able to represent the US as the dominant neighbor and the rightful claimant on Mexican land, on the basis of need and potential usage.
The US maps discussed were most probably published to educate a US audience of recent events in the US-Mexico war. In representing such events, the cartographers and publishers presented a US national narrative that defined itself in contrast to its southern neighbor. Mexico was represented throughout the narratives in the maps as a failed nation that had not yet shaken off its colonial history, was marred by political conflicts, controlled by Catholics and had failed to utilize its natural resources productively. The conquest of the northern Mexican territories was therefore justified through a narrative of Mexican failure to fully utilize them.
Repeated publications of the same maps with new additions representing the most recent events illustrated an active desire in the publishing houses to describe the recent events and incorporate them into the US national historical narratives that the maps already presented.
Paula Rebert, La Gran Línea: mapping the United States-Mexico boundary, 1849-1857, (Austin: University of Texas Press, 2001).
J. Disturnell, Mapa de los Estados Unidos de Mejico, Segun lo organizado y definido por varias actas de Congreso de dicha Republica: y construida por las mejores autoridades. (Lo Publican J. Distunerll, 102 Broadways, Nueva York, 1847).
Paula Rebert discussed the inaccuracies in: Rebert 2001 (see #1).
Timothy J. Henderson, A Glorious Defeat: Mexico and Its War with the United States, (Hill and Wang, 2008).
Rebert 2001 (see #1).
Alan Knight, "Peasants into Patriots: Thoughts on the Making of the Mexican Nation," Mexican Studies/Estudios Mexicanos10, No. 1 (1994): 135-161.
Jack Jackson, "General Taylor's "Astonishing" Map of Northeastern Mexico," The Southwestern Historical Quarterly, Vol. 101, No. 2 (Oct., 1997), pp. 143-173.
Episode 25: Mexican Migration to the U.S.
Episode 56: The Pueblo Revolt of 1680
Episode 66: Operation Intercept
Episode 73: The Borderlands War, 1915-20
Empire and Globalization
Mexican-American/Latino History
history, map, mapping, mexican-american war, mexico, propaganda, united states
University of Texas Liberal Arts Instructional Technology Services
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This 3 light floor lamp is complete with a large cream drum pleated shade. This chrome floor lamp will give a classic look placed anywhere in the house. Pair with warm or cool bulbs depending on your taste.


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text:
Challenging eLearning Best Practices: How Long Should Training Videos Be?
Posted on: June 20, 2019 /
Categories: Duration
According to conventional wisdom, and with no shortage of corroborating statistics, attention spans are shrinking — down from 12 seconds in 2000 to 8 seconds in 2015. That's barely enough time to finish this paragraph.
It's a trend that's caused quite a bit of concern, especially amongst those responsible for employee training and development. It's not hard to see the challenge: in the modern knowledge economy, where seemingly every task and function involves specialized expertise, how can companies deliver increasingly complex instructional information to an audience with an ever-shortening attention span?
By now, most everyone in Learning & Development has heard the calls to shrink the length of their emails, manuals, videos, and more in order to maximize employee engagement. And in our data-obsessed world, everyone has a theory about how long an email should be, how long a manual should be, and how long a video should be.
Is there actually a magic number for success, though?
The quest to quantify the optimal length for digital content stems from the standards traditional media outlets have seemingly perfected over decades. News publishers, television studios, and movie producers each have a sweet spot where budget, production resources, and audience attention spans intersect — 800-word feature articles, 41-second news blocks, 2-hour movies, and so on.
Generally speaking, for video online, the magic number for optimal engagement appears to be between 2 to 5 minutes. Studies examining engagement with YouTube videos consistently show solid viewership through the first 2 minutes of a video, a gradual decline in engagement after 2 minutes, and a sharper drop after 5 minutes.
But how people interact with the content on YouTube is likely a poor indicator of how they'll engage with content created for the workplace. So let's ask a better question:
How Long Should Employee Training Videos Be?
Studies of instructional video content suggest that viewer engagement follows a similar but slightly different pattern, offering corporate trainers tasked with optimizing learning amid tight budgets, limited production resources, and packed employee schedules a general guideline for producing engaging training videos.
Nearly all experts firmly believe that training videos should be as long as they need to be in order to meet learning objective; however, research suggests that 6 minutes is the optimal length for instructional videos. After 6 minutes engagement drops rapidly. In fact, another study showed that most learners won't watch video training for more than 15 minutes.
Findings like these have already caught the attention of instructional designers, and in large part these numbers are what has driven the buzz around microlearning. The idea here makes intuitive sense — if instructional content can be cut into small, self-contained modules, it will be easier for employees to identify the content that's most relevant to their needs. Likewise, if learning content is delivered in small, single-topic sessions, employees will be able to learn what they need and get back to work more quickly.
From an instructional design standpoint, microlearning even offers some nice efficiencies: training content can still be delivered in the traditional longer formats common to instructor-led learning, and with a little planning, that full session can (usually) be edited down into a series of microlearning modules. Not only are chunked video modules easier for trainers to update and modify than 30-minute long video courses, but they are also convenient for busy learners who may be viewing with a mobile device or who only need one training module to understand the material.
Is Microlearning Solving the Wrong Problem?
For all the buzz around microlearning's potential to deliver training content to shortening attention spans, a fair question remains unasked: does microlearning actually help employees learn? Or is it just a band-aid that helps make specific instructional content easier to find?
The industry consensus so far appears to be that microlearning delivers content in a way that's more aligned with how employees actually work. But availability isn't the same as effectiveness.
The simple fact remains that most roles in today's workplaces require a fair amount of advanced- to expert-level knowledge. Microlearning may be a reasonable approach to teaching people how to use the new expense reporting system, but most of what companies really want to teach their teams will be substantially more complex and nuanced. The end-to-end workings of a code base, the rigors of a new sales process, the expectations for new managers — these are subjects that cannot effectively be taught in 6 minutes or less.
Here, of course, microlearning practitioners would agree. The general approach for supporting more complex content with microlearning is to create and share a number of separate materials and either assign employees to follow a "playlist," or simply enable people to "choose their own adventures" in order to learn all there is to know.
But this brings us back to the question above. If employees have to consume many pieces of smaller content to learn the lesson, what value is the microlearning format providing?
And if it is just about making it easier to find relevant content on-demand, shouldn't we skip doing all the extra editing and planning to support microlearning and just investing in better search tools instead?
Challenging eLearning Best Practices With Video Search
When it comes to video, overwhelmingly the best practices say training videos should be short. And when the only means to search videos were metadata elements like tags and titles, that was sensible. It was difficult to know what content could be found in any video, and most companies didn't want their people watching a full 60-minute training recording when they only needed a specific 2 minutes of the content.
But that's the old way of doing things.
There's one relatively new capability that may make the question of how long a video should be completely irrelevant. Smarter video search.
Today, video search technology exists that enables learners to search the content inside video content, just as they would the web or email. Panopto's industry-leading Smart Searchtechnology, for example, uses automatic speech recognition (ASR) and optical character recognition (OCR) to index every word spoken or shown within a video. This makes it possible for employees to instantly find and fast-forward to the exact moment a relevant topic is discussed in a video, no matter its length.
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Discussion in 'BlackHat Lounge' started by christianbed, Jul 2, 2015.
Uses the Ubiquity 900 Mhz, which has long been a resource for people into this sort of thing, but I think the new device just extends the range. The idea seems to be that with the extended range, there will be an increase of users such that it will be virtually impossible to distinguish your identity from all the others.
For those not familiar with Def Con, they've got a ton of cool videos. It is an interesting group...I know that federal agencies (NSA/CIA/etc) attend as well. A few years ago there was a guy who hacked an ATM and had it spitting out money until it ran dry.
But couldn't the operator of one of the devices be shutdown and charged for violating some FCC rules?
I'm no expert in FCC law, but my feeling is 'yes'...short range radio signal broadcasting is probably no problem, but once you get to this level, it may require getting a license of some sort. Consult with your attorney first.
Great find.. Apparently, the device is used to transmit a WiFi signal not to enhance reception.. I'm always looking for a better device to enhance wifi reception (I.e, on the receiving end) Anyhow, looks pretty cool..
Yeah, i don't really see the benefit of having a long range wifi transmitter.... Having a long range adapter is a whole other story.


Don't we already have a rhetorical monkey on the banner?
Or maybe it's rhesus I'm thinking of.
With an economy the size of Turkey's, don't you think Illinois ought to be able to pay it's bills?
So, that's a huge economy in your pocket? I thought you were just glad to see us.
The National Broadband Map - takes a long time to load. But interesting in how much of the country is still without.
The National Broadband Map - takes a long time to load.
I know where you are on the map. You need broadband.
xkcd's graphs and flowcharts are the best.
It's a big sausage fest over there on the West coast. I had long heard that DC has a lot of women. It's cool to see it in a chart. And personal experience says it's true. I met my wife here.
Charles Blow put this on his most recent article.
Lots of charts, graphs, and tables.
Welcome to the cellar DanaC, you're new here, aren't you? Jim, get this person a quiz!


Snowman's lovely snowboarding game Alto's Adventure landed on Android today and it's free.
While this is a premium title on iPhone and iPad, the Android version instead has "a variety of opt-in bonuses for players who might need a little extra help, or otherwise want to show their support," according to the studio, who assures the "core gameplay experience" remains the same.
"After playing games such as Crossy Road and Threes! Free we began to understand that free-to-play can be done in a very tasteful and respectful manner," wrote Snowman co-founder Ryan Cash. "We also heard about the crazy levels of piracy on Android (thanks in part to Ustwo Games' incredibly insightful Monument Valley infographic), and wanted to ensure that Alto's Adventure could be as accessible as possible for everyone, while still being good for business."
Sounds reasonable to me. It would be wonderful to see this team continue making games.


State-controlled Russian energy group InterRao said Friday that its first-half net loss widened by 37 percent, year on year, to about 14 billion rubles ($421 million) due to depreciation charges.
If adjusted for noncash items, the firm recorded net profit of about 11 billion rubles, up 21 percent year on year, on revenue of 318 billion rubles, InterRao said in a statement.
Sales were up 21 percent, driven by acquisitions of assets in power generation and as higher selling prices helped results of its retail division. But rising costs, primarily related to higher prices for electricity it purchased, erased most revenue gains, leading to a wider operating loss than the year earlier.