text:
Vol: 14 Issue: 4
Published In: October 2018
Recent Advances of Heterogenous Radio Access Networks: A Survey
Article No: 1 Page: 345-366 doi: https://doi.org/10.13052/jmm1550-4646.1441
Yaohua Sun1 and Mugen Peng2,∗
1Student Member, IEEE
2Senior Member, IEEE
Key Laboratory of Universal Wireless Communications (Ministry of Education), Beijing University of Posts and Telecommunications, Beijing, China
E-mail: sunyaohua@bupt.edu.cn; pmg@bupt.edu.cn
∗Corresponding Author
Received 17 August 2018; Accepted 28 August 2018;
Publication 11 September 2018
As a promising paradigm to provide high spectral efficiency and energy efficiency, heterogenous radio access networks (HetNets) have attracted a lot of attention from both academia and industry. This paper presents a comprehensive survey of the recent advances in HetNets, including system architecture evolutions, key techniques, and open issues. The system architectures introduced include conventional HetNets, HetNets with cloud computing, and HetNets with fog computing. In addition, a novel performance metric, together with network self-organization and access slicing, is elaborated, which can help realize a cost-efficient HetNet. Moreover, the other state-of-the-art key techniques in HetNets are surveyed, including non-orthogonal multiple access, interference suppression, and channel estimation in the physical layer, the radio resource allocation in the medium access control layer, and clustering in the network layer. Given the extensiveness of the research area, future research opportunities are identified, which are related to access slicing, HetNets driven by deep learning, and so on.
HetNets
cost efficiency
radio resource allocation
Recent years have witnessed a drastic growth of mobile data traffic, and the heterogenous radio access network (HetNet) has been seen as a promising paradigm to cost-efficiently satisfy such traffic demand [1]. In HetNets, high power nodes (HPNs), such as macro base stations (MBSs), are responsible for wide coverage, while densely deployed low power nodes (LPNs), such as small cell base stations (SBSs), can provide high data rate in hot spots as well as seamless mobility.
However, severe intra-tier and inter-tier interference can occur due to the dense deployment of LPNs, which degrades the performance gains of HetNets, and hence it is essential to adopt some techniques to mitigate the interference. As a key technique in 4G system, the coordinated multiple points (CoMP) is proposed as an effective approach to handle the interference. Nevertheless, its performance is highly constrained by the non-ideal backhaul in real networks. Specifically, the performance of uplink CoMP has been evaluated in downtown Dresden field trials with non-ideal backhaul and distributed cooperative processing located at the base station (BS), and only 20% average spectral efficiency (SE) gains were observed. To further enhance the SE performance and reduce the energy consumption in dense HetNets, the cloud radio access network (C-RAN) is proposed, which incorporates cloud computing technology into RANs, hence facilitating large scale cooperative processing among BSs to effectively suppress interference [2]. Unfortunately, the capacity constrained fronthaul links, which connects the centralized baseband unit (BBU) pool and remote radio heads (RRHs), still have a greatly negative impact on SE and EE gains [3].
As a result, the heterogenous C-RAN (H-CRAN) is advocated in [4], whose main feature is the decoupling of data plane and control plane. Specifically, HPNs equipped with massive MIMO [5] are used to deliver control signalling, while RRHs are used for only data transmission. In this way, the burden of fronthaul links are alleviated, and thus better system performance can be achieved. In addition, the inter-tier interference between RRHs and HPNs can be effectively mitigated by taking advantage of cloud computing capability. Unfortunately, there are several unsolved issues in H-CRANs. First, with video streaming service becoming more and more popular, the fronthaul links suffer many redundant content transmissions. Second, the edge caching and computing resources are not utilized, which can help alleviate the burden on fronthaul links and the BBU pool significantly.
To overcome the drawbacks of H-CRANs, research on novel RAN architectures should be conducted. As an emerging concept, fog computing refers to the paradigm that endows network edge with storage, communication, control, configuration, measurement, and management capabilities [6]. Motivated by the superior advantages of fog computing, the author in [7] proposes fog radio access networks (F-RANs), which fully utilizes the local cache, local signal processing, and local radio resource management at edge devices like smart user equipments (UEs) to alleviate the burden of fronthaul links. Note that redundant traffic over fronthaul can be effectively avoided by proactively caching popular contents at local cache.
Despite the newly emerging RAN architectures, new performance metrics and other techniques are essential to further enhance the system performance and create a cost-efficient, flexible wireless network with high business value. Given the key role of HetNets in 5G [8] and the recent study achievements, an overview of the architectures, performance metrics, and key techniques of HetNets is presented in this paper. Specifically, RAN architectures include traditional HetNets, H-CRANs, and F-RANs, and a new performance metric called economical energy efficiency, together with access slicing and network self-organization, will be introduced to improve the network cost efficiency. In addition, other key techniques across the physical, MAC, and network layers will be elaborated as well.
Table 1 Summary of Abbreviations
BS base station
BBU baseband unit
CAPEX capital expenditures
C-RAN cloud radio access network
CSI channel state information
D2D device-to-device
EE energy efficiency
ESE economical spectral efficiency
eMBB enhanced mobile broadband
F-AP fog access point
F-RAN fog radio access network
F-UE fog user equipment
H-CRAN heterogenous C-RAN
HetNet heterogenous network
HPN high power node
HUE HPN user equipment
IC interference collaboration
LPN low power node
MBS macro base station
mMTC massive machine-type communications
MSE mean square error
MUE macro user equipment
NOMA non-orthogonal multiple access
OPEX operational expenditures
QoS quality of service
RB resource block
RRH remote radio heads
RRM radio resource management
SBS small cell base station
SDN software-defined-networking
SE spectral efficiency
SON self-organizing networks
UE user equipment
uMTC ultra-reliable machine-type communications
The remainder of this paper is organized as follows. Section 2 describes the evolution of HetNet architectures. Section 3 will introduce a novel performance metric named as economical energy efficiency, network self-organization, and access slicing, which can all lead to a more cost-efficient network. Section 4 presents several other key techniques in HetNets, including channel estimation, non-orthogonal multiple access (NOMA), interference suppression, radio resource allocation, and BS clustering. Section 5 identifies several research directions to facilitate future research activities, which are regarded to HetNets driven by deep learning, software-defined-networking (SDN), and so on. Finally, the conclusion is made in Section 6. For convenience, some important abbreviations are listed in Table 1.
2 The Architecture Evolution of HetNets
In this section, various HetNet architectures are introduced, including traditional HetNets, H-CRANs, and F-RANs.
2.1 The Architecture of Traditional HetNets
The architecture of a traditional HetNet is illustrated in Figure 1. When subchannels are shared between different tiers, the UEs accessing small cells, such as femtocells, can suffer severe interference from the MBS, and there can also exist intra-tier interference, such as the interference between pico-cells. To reduce the interference and hence improve system performance, interference coordination and radio resource management (RRM) can be adopted. More concretely, the interference coordination techniques in the physical layer further includes zero-forcing beamforming [9], interference alignment [10], and so on. In addition, by properly allocating power and subchannel resources to different UEs based on their CSI characteristics, RRM can also help mitigate interference. Moreover, handover control and cell range expansion can help better balance the loads among heterogenous nodes, and the deployment of relay nodes can effectively expand the coverage area of the network.
Figure 1 A HetNet Architecture for E-UTRAN systems.
To enhance the performance of cell-edge users, a hierarchical HetNet can be employed, which consists of a hierarchical cooperative basic layer, a homogeneous cooperative enhanced layer, and a heterogeneous cooperative extended layer. In the cooperative basic layer, high-order modulation and coding schemes are utilized to provide cell-edge users with unicast services of high data rate, while hierarchical modulation schemes with unequal error protection space-time code are used for multicasting. In the homogeneous cooperative enhanced layer, user performance is improved by cooperative homogeneous diversity. For the heterogeneous cooperative extended layer, the convergence and interworking of multiple RANs are ensured by heterogeneous cooperative diversity gain.
2.2 The Architecture of H-CRANs
The H-CRAN architecture is illustrated in Figure 2. Similarly to the traditional C-RAN, many RRHs in H-CRANs can perform large scale cooperative transmission owing to the centralized BBU pool, and each RRH possesses low power consumption, since it keeps only the radio frequency and simple symbol processing functionalities. The BBU pool is responsible for executing the remaining physical and upper layer procedures. Nevertheless, there is a big difference between H-CRANs and C-RANs, which lies in the involvement of interfaces between the BBU pool and HPNs. Specifically, S1 and X2 interfaces are defined as the data and control interfaces, respectively, which follows the definitions of 3GPP. Such interfaces facilitate mitigating the inter-tier interference between HPNs and RRHs using centralized cloud computing based cooperative processing techniques. Moreover, both voice and data requirement can be satisfied by H-CRANs, considering the ability of packet switch mode in 4G systems to support voice service. Meanwhile, HPNs control the voice service, while RRHs are deployed to support packet traffic of high data rate.
Figure 2 The system architecture of H-CRANs.
By moving the delivery of control signallings and system broadcasting information to HPNs, the capacity and latency constraints of fronthaul links can be alleviated, and RRHs can dynamically go into sleep mode according to the network traffic volume, which will decrease the system energy consumption. Besides transmitting control information, HPNs are exploited to support burst traffic and other services with low data rate. Furthermore, a great number of overheads can be saved during radio connection/release by evolving the pure connection-oriented mechanism to an adaptive one, and HPNs can also be equipped with massive MIMO to enlarge the coverage and raise the capacity. To achieve high data rate for RRH transmission, different techniques in the physical layer can be utilized, including millimeter wave [11], optical light [12], and so on.
Figure 3 The system architecture for implementing F-RANs.
2.3 The Architecture of F-RANs
As shown in Figure 3, the F-RAN architecture features fog access points (F-APs) in the access layer and fog UEs (F-UEs) in the terminal layer, which can conduct local caching, local signal processing, and local RRM. For adjacent F-UEs, such as F-UE13 and F-UE11 in Figure 3, they can either communicate directly (device-to-device (D2D) mode) or communicate with the help of another F-UE working as a relay (relay mode). In addition, HPNs and FAPs constitute the access layer, which are responsible for delivering control signalling to F-UEs and forwarding the UE data to the BBU pool over fronthaul links, respectively. Moreover, X2/S1 interface is defined for the backhaul links connecting the BBU pool with HPNs and is backward compatible with that defined in 3GPP standards for LTE and LTE-Advanced systems.
Different from H-CRANs, the burden on the fronthaul and the BBU pool can be greatly reduced by the F-RAN architecture, owing to its capability of processing signal and managing radio resource locally at FAPs and FUEs. Furthermore, by utilizing big data analysis on the user content requests, popular contents can be cached at F-APs and F-UEs in advance, and hence some future requests can be served locally. Considering the popularization of video streaming services, local caching can effectively avoid redundant traffic transmission for these services, and hence alleviate the fronthaul constraints. At last, local caching, local signal processing, and local RRM can better satisfy the low latency requirement of some applications in the 5G era.
3 Towards Cost-Efficient HetNets
Although the technical upgrade has bring users better quality of service (QoS), it is reported that the capital expenditures (CAPEX) and operational expenditures (OPEX) will excess the operator's profits in the future [13]. Hence, in addition to caring about the traditional performance metrics like SE and EE, ever-increasing network cost should also be paid attention to. To this end, a new metric called economical energy efficiency will be introduced, and two techniques, named as self-organizing networks (SON) and access slicing, will be presented, which also both help achieve cost-efficient HetNets.
3.1 Economical Energy Efficiency
Traditional network performance metrics like SE and EE do not take the network cost into account, such as the cost led by the deployment of edge cache and transport networks, which, however, directly impacts the profits of network operators. To evaluate the HetNet in a more comprehensive manner, the author in [14] proposes a novel performance metric, named as economical energy efficiency, which is defined as the ratio of effective system throughput to energy consumption weighted by cost coefficients. Specifically, the weighted energy consumption is a sum of two parts. The first part is the dynamic energy consumption of all the BSs, which relies on the load of each BS, while the second part is the sum of per-BS static power consumption multiplied by a cost coefficient that reflects the heterogeneity of BSs in edge cache resource and X-haul solutions. Numerical results demonstrate that the overuse of X-Haul capacity and edge cache resource has no impact on the SE and EE metrics, while the proposed metric can reflect all the impacts brought by throughput, energy consumption, and affordability.
3.2 Self-Organizing HetNets
As a promising technique, network self-organization endows HetNets with intelligence, which makes it possible to realize the automatical management and optimization of coverage, capacity, spectrum, and so on, and hence the OPEX of operators can be reduced. In general, the self-organization of HetNets includes the following three aspects: self-configuration, self-optimization, and self-healing [15]. To take full advantage of both centralized and distributed SON architectures, the author in [16] proposes a hybrid SON architecture, and differences between traditional homogeneous networks and HetNets are discussed in terms of self-configuration as well as self-optimization. Since the deployment of a completely self-organized HetNet is non-trivial due to the time-varying traffic, SON coordination in HetNets is essential to be investigated. Specifically, efficient interaction and coordination of SON mechanisms can be realized based on the graph-based decision framework proposed in [17], which describes the interaction and conflict relationships between multiple SON mechanisms using metric event and action graph. Then, for a given event or a combination of events, the strategy-based SON coordination in HetNets can be easily implemented.
3.3 Access Slicing
As a cost-efficient solution to support diverse use cases in the 5G era, network slicing enables the provision of networks in an as-a-service fashion [18]. However, current studies mainly concentrate on core networks based slicing, and the impact of the characteristics of RANs is not well considered. To get a more effective network slicing solution, the author in [19] proposes an enhanced network slicing approach in F-RANs, termed as access slicing. The proposed access slicing architecture is shown in Figure 4, where the networking strategies of three typical scenarios are demonstrated, including massive machine-type communications (mMTC), ultra-reliable MTC (uMTC), and enhanced mobile broadband (eMBB). In the mMTC scenario with massive connections, adjacent UEs are formed into a cluster, and one of the UEs is selected as the cluster head to deliver the traffic of UEs in the cluster to F-APs or HPNs. Moreover, F-APs with caching capability and D2D communication are exploited to achieve low latency in the uMTC scenario, while RRHs cooperatively serve UEs in the eMBB scenario to achieve high data rate.
Meanwhile, the proposed architecture is featured with a centralized orchestration layer and a slice instance layer to guarantee diverse QoS and quality of experience requirements. Specifically, the orchestration layer is responsible for identifying the resource proportion assigned to each slice instance, and the resources can be allocated to slice instances either in an orthogonal or shared manner. In the slice instance layer, each access slice instance controls its own operation and resource allocation. If the dedicated resource can not meet the communication demand, the access slice instance will use the resource shared with other slice instances while limiting the influence on these slice instances.
4 Other Key Techniques
Besides the performance metric and techniques introduced above, other techniques, such as channel estimation in the physical layer and RRM in the MAC layer, are also the key to boosting HetNet performance, which will be presented in this section.
Figure 4 An illustration of access slicing in F-RANs.
4.1 Key Techniques in the Physical Layer
1) Channel Estimation: In wireless networks, the acquisition of channel state information (CSI) is essential for resource allocation and interference suppression. To achieve a high estimation accuracy while reducing training overhead, the author in [20] proposes a superimposed-segment training design for the uplink of a network with multiple RRHs that connect to the BBU pool via wireless fronthaul, and develops a maximum a posteriori probability channel estimation algorithm. The core idea of the proposal is that each UE superimposes a periodic training sequence on the data signal, and a separate pilot is prepended to the received signal at each RRH before the signal is forwarded to the BBU pool. By simulation, it is demonstrated that the estimation mean square error (MSE) can be decreased effectively.
Although the proposal in [20] achieves a good performance, there is still room for performance improvement, due to the segment sharing between data and training transmissions. Therefore, the author in [21] studies segment training based channel estimation for an uplink C-RAN. By optimizing the MSE of channel estimation under the power constraint, the training sequence design can be derived for radio access links and wireless fronthaul links. Moreover, to improve the system SE, the lengths of the segment training sequences are optimized.
2) Non-Orthogonal Multiple Access: By enabling multiple users to transmit over the same frequency and time resource at the same time, NOMA can greatly increase the SE and the number of connections for HetNets [22]. In [23], based on stochastic geometry and Gauss-Chebyshev integration, the outage probability is rigourously analyzed for a downlink NOMA-enabled C-RAN with RRHs uniformly distributed in a disk and two users paired to implement NOMA. Via simulations, the correctness of the closed-form expression of the outage probability is verified, and the superiority of the proposal is demonstrated, compared with an opportunistic multiple access scheme and a time division multiple access scheme. While in [24], an NOMA based multicast scheme is proposed for an F-RAN, which promises pushing and multicasting content objects simultaneously, thus leading to a high SE. Using stochastic geometry, the author gives an explicit expression of outage probability for a multi-cell scenario, which shows that the NOMA based multicast scheme outperforms the conventional orthogonal multiple access based multicast scheme.
3) Interference Suppression: In HetNets, when resource blocks are shared among heterogenous nodes, severe inter-tier interference can exist. To suppress the inter-tier interference between RRHs and the HPN in a downlink H-CRAN, two precoding schemes, named as interference collaboration (IC) and beamforming, are investigated for the multi-antenna HPN in [25]. The IC scheme aims at canceling the interference incurred by the transmission of each macro UE (MUE) to all the other MUEs and RRH UEs, while the beamforming scheme intends to only maximize the signal gain for the target UE regardless of the interference to other UEs. By utilizing stochastic geometry, closed-form expressions of the overall outage probabilities, system capacities, and average bit error rates are derived for these two schemes, respectively. Via simulation, it is concluded that the performance gap between IC and beamforming schemes is related to the number of antennas at the HPN and the number of RRHs.
4.2 Radio Resource Management in the MAC Layer
Effective RRM approaches are essential to fully utilize the limited radio resource in HetNets to meet user QoS. However, resource optimization problems in HetNets are often non-convex and hard to solve due to intra-tier and inter-tier interference, the involvement of binary variables, and so on. Faced with this challenge, authors in [26] and [27] employ various mathematical theories to transform the non-convex problems to more tractable ones. More concretely, in [26], soft fractional frequency reuse is improved for an orthogonal-frequency-division-multiple-access H-CRAN, where RRH UEs with low QoS requirements use the same resource blocks (RBs) with HPN UEs (HUEs). Then, an optimization problem of RB and power allocation is formulated, which aims at maximizing system EE performance. Since the optimization objective is in a fractional form, the formulated problem is non-convex. To handle the non-convexity, the author transforms the primal problem into an equivalent convex feasibility problem, and closed-form resource allocation solutions are derived via Lagrange dual decomposition. While in [27], the secure capacity of D2D pairs is optimized in a D2D underlaying, traditional HetNet. Because of the binary subcarrier allocation variables, the original problem is NP-hard. However, it is observed that the problem under pre-determined subcarrier allocation can be transformed into a convex power allocation problem according to the Perron-Frobenius theory. Therefore, the author is motivated to first derive a sub-optimal subcarrier allocation solution, and then the optimal power allocation problem is solved.
Furthermore, to achieve a better global performance, it is necessary to take into account both the conventional performance metrics in the physical layer and the traffic delay in the upper layer. As a simple framework to deal with delay-aware RRM optimization problems, the Lyapunov optimization approach has been commonly adopted in HetNets. In [28], the author studies the trade-off between throughput utility and delay performance in a slotted H-CRAN. Specifically, a joint stochastic congestion control and resource allocation problem is formulated, which is transformed and decomposed into three separate subproblems that can be concurrently solved at each slot by using the Lyapunov method. Via theoretical analysis, it is shown that the quantitative control of throughput-delay performance trade-off can be achieved with guaranteed EE performance. Instead of taking the EE performance requirement as a constraint in the formulated problem, the author in [29] aims at maximizing an averaged weighted EE utility objective function directly for H-CRANs under the constraints of inter-tier interference to HUEs and fronthaul capacity constraints. Also leveraging the Lyapunov approach, the primal problem is transformed into a per-slot network-wide cooperative beamformer optimization problem.
In addition to the above works, there is another branch of research that focuses on joint communication mode selection and resource allocation for D2D enabled HetNets. In particular, F-RANs have various ways of communication available to UEs, such as D2D communication mode, HPN mode, global C-RAN mode, and so on, and communication mode selection is generally coupled with resource allocation [30]. With the goal of alleviating the burden on the BBU pool, a distributed reinforcement learning based mode selection and subchannel allocation approach to improving SE is proposed for an uplink F-RAN in [31], which enables each D2D pair to autonomously decide its communication mode and utilized subchannel based on only the received individual utility. The adopted learning algorithm features learning the utility and strategy at the same time based on extremely simple calculations. While in [32], a stochastic optimization problem is investigated under the dynamic CSI and queue state information, whose objective is to maximize the overall average system throughput of F-RANs. Then, this problem is further transformed into a per-slot optimization problem by Lyapunov optimization, which is further decoupled into three sub-problems: the mode selection problem, beamforming design for UEs in global C-RAN mode, and power control for UEs in D2D mode. Based on this decoupling, an iterative algorithm is proposed.
Different from all the above works mainly optimizing traditional performance metrics, a novel performance metric called economical SE (ESE) is maximized for a downlink C-RAN in [33], which takes the energy consumption, fronthaul cost, and system throughput into account. To solve the non-convex ESE maximization problem with fronthaul capacity and transmit power constraints, a beamforming design algorithm containing an outer loop and an inner loop is proposed. In the outer loop, bisection search is utilized to transform the primal problem into an equivalent subproblem, which can be then efficiently solved by a weighted minimum mean square error approach in the inner loop. While in [34], resource allocation for access slicing in an F-RAN is studied, where two kinds of slice instances are considered, namely an eMBB slice and an URLLC slice. By the proposed two-step iterative algorithm that is based on the Hungarian method, linear integer programming, and geometric programming, subcarrier allocation among UEs accessing the two slices and UE association together with content placement in the URLLC sice are optimized, which minimizes the latency performance for the URLLC slice while satisfying the data rate requirement of the eMBB slice.
4.3 Clustering Schemes in the Network Layer
In wireless networking, it is sometimes beneficial to group BSs into different clusters to attain cooperation or coordination gain within each cluster. To develop a low complexity clustering algorithm, coalitional game based algorithms are often utilized [35]. In [36], an intra-tier interference coordination scheme is proposed for a dense small cell network. Specifically, multiple SBSs can form clusters, within each of which the SBS transmissions are scheduled based on time-division-multiple-access. Then, the interaction among SBSs to form clusters is modeled as a coalition formation game in partition form, aiming at maximizing the individual SBS throughput, and a merge and split based algorithm with partial reversibility rule is developed to achieve a stable partition. In [37], a transfer order based coalition formation algorithm is used to efficiently group FAPs into clusters, and the FAPs in the same cluster perform zero-forcing beamforming cooperatively. The novelty of this game formulation lies in the consideration of cluster formation cost incurred by fetching the missing contents requested by the UEs in the cluster, which can greatly limit the cluster size. Simulation result reveals that increasing the edge caching capacity at FAPs can improve the system SE, since the cost constraint of cluster formation is alleviated, and hence the potential cluster size becomes larger, reducing more intra-tier interference.
5 Future Research Directions
In this section, motivated by the recent trends of wireless networks, several future research opportunities are pointed out.
5.1 HetNets Driven By Deep Learning
As an important technology for enabling artificial intelligence, deep learning [38] has been successfully applied in many areas, including computer vision and speech recognition, and has drawn a lot of attention of researchers in the wireless communication area. In [39], a learning framework is proposed in which convolutional neural networks and recurrent neural networks are used to extract features in spatial domain and time domain from raw information collected by wireless networks, respectively, and this manner avoids identifying features manually. Taking the extracted features as the state input, deep reinforcement learning is adopted to control wireless networks intelligently, and the superiority of the proposal is verified by applying it to mobility management in wireless local area networks. However, to apply deep learning in HetNets, more studies should be conducted. For example, theoretical analysis should be done to provide guidelines on the architecture design of neural networks and the selection of hyper-parameters. Furthermore, the infrastructure of HetNets needs to be upgraded to support the implementation of deep learning algorithms, especially at the network edge to facilitate real-time network optimization and control.
5.2 Software-Defined Networking and Network Function Virtualization
As two attractive technologies, SDN decouples the control plane from the data plane via controllers [40], while network function virtualization (NFV) aims to decouple network functions from the underlying proprietary hardware [41]. With SDN and NFV, future HetNets can be endowed with high flexibility and reconfigurability, and network functions implemented by softwares can be provisioned on demand, which can both help reduce CAPEX and OPEX and speed up the deployment of new services. In the future, it is interesting to study the practical design of the SDN paradigm for HetNets to achieve intelligent resource management, mobility management, and so on. For the NFV, efficient resource orchestration schemes are expected to better support access slicing in HetNets and meet diverse customization requirements of slice instances.
5.3 Multi-Dimension Resource Allocation
With the convergence of information technology and communication technology, the available resource in future HetNets will further include cache resource and computing resource. Intuitively, to achieve a better global system performance, radio resource, cache resource, and computing resource should be jointly optimized. In [42], the connection between cache resource and radio resource is established by a cache-based adaptive rate requirement. However, such modeling is very simplified, and hence the system model for linking up various resources needs to be further explored. Another big challenge when handling multi-dimension resource allocation is that different kinds of resources can be allocated at different time scales, which makes the problem very complicated [43].
5.4 Access Slicing in HetNets
Although the literature [19] makes a big step for network slicing, several challenges exist for its implementation. First, as stated in [19], the information awareness allows the access slice orchestration layer make intelligent decisions on slice instance creation and resource management. Nevertheless, more specifications should be investigated like the specific information type needed, the frequency for collecting each type of information, and the way of slice instance configuration based on the collected and processed information. Second, resource allocation between multiple slices should be investigated to fully utilize the resources of HetNets to meet diverse performance requirements. However, such a problem can be very challenging, since it is basically a multi-objective optimization problem. Meanwhile, besides allocating radio resource, some slices need cache resource and computation resource, which incurs extra challenges.
5.5 Enabling IoT Services in HetNets
In the 5G era, the IoT services will have more stringent performance requirements, whose scenarios include mMTC and uMTC [44]. The former needs to support massive connections, while the latter should ensure low-latency and highly reliable data transmission. To better support mMTC services, HetNets can be enhanced by incorporating massive MIMO in the physical layer [45] and cluster formation in the network layer [19]. For uMTC services, they can be benefited from HetNets with edge computing capability, such as F-RANs. By moving data analysis and decision-making to the network edge, low latency can be achieved. Nevertheless, there are still many challenges to enable mMTC and uMTC services. For example, although the cluster based access strategy can raise the number of connections, effective and low-complexity cluster formation schemes need to be further investigated. Moreover, computing task allocation algorithms should be developed to assign data analysis or decision making tasks to different fog nodes, taking into account the data availability, computing capability, and communication link condition of each FAP.
This paper makes an overview of state-of-the-art system architectures, novel performance metrics, and key technologies of heterogenous networks (HetNets). The architectures surveyed include traditional HetNets, heterogenous cloud radio access networks, and fog radio access networks, and the economical energy efficiency metric, network self-organization, and access slicing can all help lead to a more cost-efficient HetNet. In addition, other key techniques in the physical, MAC, and network layers, such as non-orthogonal multiple access and radio resource management, are introduced as well. Furthermore, given the extensiveness of the research areas, several future research opportunities are outlined, in terms of the application of deep learning, software-defined-networking, network function virtualization, multi-dimension resource allocation, and so on.
This work was supported in part by the State Major Science and Technology Special Project under 2017ZX03001025-006 and 2018ZX03001023-005, the National Natural Science Foundation of China under No. 61831002, and in part by the National Program for Special Support of Eminent Professionals.
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[15] Imran, A., Zoha, A., and Abu-Dayya, A. (2014). "Challenges in 5G: How to empower SON with big data for enabling 5G," IEEE Netw., 28(6), 27–33.
[16] Peng, M., Liang, D., Wei, Y., Li, J., and Chen, H. (2013). "Self-configuration and self-optimization in LTE-advanced heterogeneous networks," IEEE Commun. Mag., 51(5), 36–45.
[17] Gelabert, X., Sayrac, B., and Jemaa, S. B. (2014). "A heuristic coordination framework for self-optimizing mechanisms in LTE HetNets," IEEE Trans. Veh. Technol., 63(3), 1320–1334.
[18] Samdanis, K., Costa-Perez, X., and Sciancalepore, V. (2016). "From network sharing to multi-tenancy: The 5G network slice broker," IEEE Commun. Mag., 54(7), 32–39.
[19] Xiang, H., Zhou, W., Daneshmand, M., and Peng, M. (2017). "Network slicing in fog radio access networks: Issues and challenges," IEEE Commun. Mag., 55(12), 110–116.
[20] Xie, X., Peng, M., Wang, W., and Poor, H. V. (2015). "Training design and channel estimation in uplink cloud radio access networks," IEEE Signal Process. Lett., 22(8), 1060–1064.
[21] Hu, Q., Peng, M., Mao, Z., Xie, X., and Poor, H. V. (2016). "Training design for channel estimation in uplink cloud radio access networks," IEEE Trans. Signal Process., 64 (13), 3324–3337.
[22] Ding, Z., Peng, M., and Poor, H. V. (2015). "Cooperative non-orthogonal multiple access in 5G systems," IEEE Commun. Lett., 19(8), 1462–1465.
[23] Gu, X., Ji, X., Ding, Z., Wu, W., and Peng, M. (2018). "Outage probability analysis of non-orthogonal multiple access in cloud radio access networks," IEEE Commun. Lett., 22(1), 149–152.
[24] Zhao, Z., Xu, M., Li, Y., and Peng, M. (2017). "A non-orthogonal multiple access based multicast scheme in wireless content caching networks," IEEE J. Sel. Areas Commun., 35(12), 2723–2735.
[25] Peng, M., Xiang, H., Cheng, Y., Yan, S., and Poor, H. V. (2015). "Inter-tier interference suppression in heterogeneous cloud radio access networks," IEEE Access, 3, 2441–2455.
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Yaohua Sun (S'18) received the bachelor's degree (with first class Hons.) in telecommunications engineering (with management) from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2014. He is a Ph.D. student at the Key Laboratory of Universal Wireless Communications (Ministry of Education), BUPT. His research interests include game theory, resource management, deep reinforcement learning, network slicing, and fog radio access networks. He was the recipient of the National Scholarship in 2011 and 2017, and he has been reviewers for IEEE Transactions on Communications, Journal on Selected Areas in Communications, IEEE Communications Magazine, IEEE Communications Letters, and IEEE Internet of Things Journal.
Mugen Peng (M'05, SM'11) received the Ph.D. degree in communication and information systems from the Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2005. Afterward, he joined BUPT, where he has been a Full Professor with the School of Information and Communication Engineering since 2012. During 2014 he was also an academic visiting fellow at Princeton University, USA. He leads a Research Group focusing on wireless transmission and networking technologies in BUPT. He has authored and co-authored over 90 refereed IEEE journal papers and over 300 conference proceeding papers. His main research areas include wireless communication theory, radio signal processing, cooperative communication, self-organization networking, heterogeneous networking, cloud communication, and Internet of Things.
Dr. Peng was a recipient of the 2018 Heinrich Hertz Prize Paper Award, the 2014 IEEE ComSoc AP Outstanding Young Researcher Award, and the Best Paper Award in the JCN 2016, IEEE WCNC 2015, IEEE GameNets 2014, IEEE CIT 2014, ICCTA 2011, IC-BNMT 2010, and IET CCWMC 2009. He is currently or have been on the Editorial/Associate Editorial Board of the IEEE Communications Magazine, IEEE ACCESS, IEEE Internet of Things Journal, IET Communications, and China Communications. He received the First Grade Award of Technological Invention Award three times in China.
Journal of Mobile Multimedia, Vol. 14_4, 345–366.
doi: 10.13052/jmm1550-4646.1441
© 2018 River Publishers


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About 5 years ago wind farms started popping up in Colorado. They are easy to spot from the air and can be seen for many miles because the structures are so enormous. A modern wind turbine can reach well over 400 feet into the air.
Over a span of less than 3 decades wind turbines have increased from an average rotor size of 10 meters generating 25KW to a rotor size of 112 meters generating up to 6 MW per turbine. The 3+ MW turbines are generally installed in the ocean about 5-10 miles off shore where the wind is steady. Land based wind turbines are generally rated at less than 2 MW. Each 1 MW of wind energy is enough power for approximately 300 households based on an average U.S. household consumption rate of around 900 kWh per month. The average power available from wind turbines, also called their 'capacity factor', is about 1/3 of their rated generating capacity because the wind is not constant. They make their rated power in winds about 20-25 mph and will not generate any more than the rated power even when the wind increases above that speed because it would over stress the system. Above about 55-60 mph, the wind turbine will protect itself by locking its rotor so that the blades will not get damaged.
Here in Colorado there are many locations where the wind is nearly always blowing on the plains and there aren't any obstructions on the ground to slow it down. Other than the trees along the Platte river, there is virtually no vegetation taller than a cornstalk between here and Nebraska. And Wyoming has even higher wind and fewer trees than Colorado in the eastern side of the state.
I was out flying today and took a few photos of the Ponnequin Wind Farm up near the border of Wyoming to take a look at Colorado's first wind farm which was built about 6 years ago. You can see a photo of it below.
If you're curious about the Ponnequin wind farm you can read a little about it here at the American Wind Energy Association website. If you have Google Earth, you can get a satellite view of it here. Or here on Google Maps.
About 60 miles east of the Ponnequin Wind Farm, the largest wind farm in Colorado called Cedar Creek is currently under construction. During the past few months I've been monitoring the progress of it since it's not far from Greeley, at least not as the crow (or LongEZ) flies, and I'm astonished at how fast it's getting built. A few months ago, there were only a few towers standing and now they have more than 200+ towers erected. I timed how long it took me to fly from one end to the other and it was 5 minutes, flying at 160 miles an hour which means it stretches 13 miles from east to west. It's on land that previously didn't have much use due to sparse population and lack of water for irrigation. I did some quick calculations and realized that this wind farm will generate more than $80M/year in electricity from its 273 wind turbines, based on the average of around $.10/KWh currently paid in the U.S. by consumers. That's not a bad return since the fuel, a major cost for conventional power plants, is free in the case of wind power.
Looking down from above on these structures, it's hard to fathom how big they are. In the picture above, the closest wind turbine has a large truck parked near its base which is virtually invisible in the photo. That gives you an approximate idea of the scale of these structures. Go ahead and click on that image to get a higher resolution photo of it and a better idea of the relative size of the truck and the wind turbine.
One of the biggest logistical issues with constructing these wind farms is getting the materials to the site. The tower sections take up most of the highway when transported and are nearly 60 feet long per section. It takes 3 of them to make up the tower. Similarly, the rotor blades are enormous, over 100 feet in length. Can you imagine trying to get that to go around a corner?
Vestas Wind Systems of Denmark is currently building a turbine blade plant in Windsor, CO, just about 10 miles from where I live. This will better accommodate the delivery of large turbine blades to wind farm sites in the western U.S.
In talking with a rancher in Wyoming recently, it appears that a lot of site surveys have been underway over the years to construct wind farms there, turning what was very marginal land into a valuable resource to provide clean, renewable energy for future generations. Even though the Cedar Creek site seems expansive, and it is, there is so much land in eastern Colorado and Wyoming that would be ideal for wind farms that it's easy to imagine building them for the next 20 or 30 years. That's about the average life expectancy of the wind generating equipment, so after they've done that, they will get to do it over again.
This entry was posted in energy, wind by Lee Devlin. Bookmark the permalink.
Hi Connie, When I arranged a group trip to Ponnequin wind farm a few years ago, I called Xcel's PR department and they put it together for us. They do a number of them per year and so you should be able to call and get an appointment.
I realize the last comment was nearly 2 years ago. I have a couple or so of questions and a comment or two. I've been reading articles about renewable energy; do you consider wind farms more habitat and environmental friendly? The proposed solar installations outside of Mojave National Preserve would take up an awful lot of land. I was there last summer and frankly can see the eyesore that would create. Are wind towers and turbines a safer alternative to birds and endangered species? I can see a bird flying into one of the blades, especially if it's on the run from another bird, but the blades move so slowly, I would think any bird can get past it without going through the rotation. Also is it safe for grazing wildlife, horses and cattle. It would be a great alternate income for the BLM if environmental impact studies come back in the positive. That the BLM has voted no to the solar facility south an west of Baker is just the first step, but there are other studies that could blow the BLM out of the running. I live in Greeley, Colorado and see the towers and blades coming down the highway from time to time. There was a Vestas plant in Ft. Lupton. There's one now in Windsor, as mentioned in your article. I've seen wind farms in eastern New Mexico and acres and acres of them in western Texas. They look a bit alien. Most towns have lots of ordnances property owners have to jump through to get a wind turbine by their house. When you look around at all the trees and poles and lines, it doesn't look good logistically. I've seen the ones in southern Wyoming at Medicine Bow, even camped under a couple of them. Very quiet. Cattle were grazing without upset, and it's wide open space, I think BLM land. And can be pretty windy on the eastern 2/3 of Wyoming What is your opinion on the environmental impact on land and wildlife? I like the idea of wind farms on open space rather than solar installations taking up a lot of open space.
Hi Darlene, I am a proponent of wind power as well as solar power but recognize they are not a replacement for conventional sources, just a way to reduce the rate at which conventional fossil fuels are depleted. Wind power is only practical in areas have good average wind speed and where they can be arranged in a way that they can utilize common infrastructure. This is whey they are generally arranged in large groups. I don't mind their appearance, but I understand that some people feel they are an eyesore. They can only be a part of the power grid, since wind power is not persistent and so when they are generating, fuel-based generation systems can be conserved, but when no wind is blowing they have to be backed up by fuel-based sources.
Wind turbines are not a serious threat to bird populations. Windows, cars, and cats kill more than a thousand times more birds per year than wind turbines.


text:
Q: accessing contents of a relationship fault in Core Data I'm debugging an error that involves Core Data and am having trouble viewing the contents of the information retrieved.  For example, when entering "po localSchedules" in the Xcode debugger console, this is what I get:
<Schedules: 0x1702c6d60> (entity: Schedules; id: 0xd000000000340008 <x-coredata://D32DD2B0-A69F-45B4-9856-7D935B41718D/Schedules/p13> ; data: {
    accessorySchedule = "<relationship fault: 0x1700362a0 'accessorySchedule'>";
    name = "TEST";
})

How can I view the contents of accessorySchedule?  "<relationship fault: 0x1700362a0 'accessorySchedule'>" is not very informative.  Here's some relevant code:
NSManagedObjectContext *managedObjectContext = [self managedObjectContext];
NSArray *localSchedules = [self fetchLocalSchedulesInManagedObjectContext:managedObjectContext error:&fetchError];

Using the solution from here, I implemented the following:
for (Schedules *schedule in localSchedules) {
    for (AccessorySchedule *accessorySchedule in [schedule accessorySchedule]) {
        NSLog(@"RESULT - %@", accessorySchedule);
    }
}

But, the result was also not informative:
RESULT - <AccessorySchedule: 0x1740b6da0> (entity: AccessorySchedule; id: 0xd00000000040000a <x-coredata://D32DD2B0-A69F-45B4-9856-7D935B41718D/AccessorySchedule/p16> ; data: <fault>)

In the above, how can I see what's stored in data: <fault>?

A: I don't no this one is help full for u r issue
[request setReturnsObjectsAsFaults:NO] We have to set request  is to carring the objects we have to decode that .hope this will help you .

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Newcastle United Football Fans
Ha'way the Lads!
NEWCASTLE UNITED LOSE THREE SUCCESSIVE GAMES
Leave a Comment on NEWCASTLE UNITED LOSE THREE SUCCESSIVE GAMES
Newcastle United have lost three Premier League games on the bounce after they suffered a 5-1 comeback defeat against hosts Tottenham Hotspur at the new Tottenham Hotspur Stadium on Sunday. The run of three straight defeats in the league came on the back of a nine-game unbeaten run. Newcastle United had the best unbeaten run amongst the Premier League teams before it was ended by Chelsea last month.
European champions Chelsea stopped the Magpies unbeaten league run thanks to a 1-0 home win. German attacker Kai Havertz scored the only goal of the match with a minute left on the clock.
The slim defeat to Chelsea was followed by a similar defeat at the hands ofstruggling Everton. The Toffees were able to record a 1-0 win despite playing a significant part of the game with ten men after Brazil international Allan was sent off for a tackle on French winger Allan Saint-Maximin. Nigerian star Alex Iwobi scored the winning goal deep into injury time to bag three precious points for the Toffees in their fight to stay in the Premier League.
Newcastle United were handed a big defeat at Tottenham Hotspur to make it three losses in a row despite taking the lead during the game. Swiss defender Fabian Schar scored a free-kick to give Newcastle United the lead six minutes from the break, but they were unable to hold on to that advantage after Welshman Ben Davies scored the equaliser for the home team four minutes after the opening goal from Schar. The headed goal from Welsh international Davies ensured that Spurs and Newcastle United went into the break on level terms. Tottenham Hotspur went on to take the game away from the Magpies in the second half after scoring four times through Matt Doherty, Emerson Royal, Son Heung-Min and Dutch substitute Steven Bergwijn.
PARDEW WANTS CARROLL
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text:
Home » Articles » Expert Details What China Does After Stealing IP
DSEI JAPAN NEWS: Expert Details What China Does After Stealing IP
Image: iStock
MAKUHARI, Japan — China's role in stealing Western companies' intellectual property is well documented. However, not too many of the victims know what the nation does with trade secrets after they are pilfered, a former U.S. government leader said Nov. 19.
"China has institutionalized a system that combines legal and illegal means of technology acquisition from abroad," said William Schneider Jr., former undersecretary of state for security assistance, science and technology and former chair of the Defense Science Board.
It's well known that China steals IP and other secrets from industries, academia and the government, "but what is not so well known is how China converts the technology it acquires into their military capabilities," he said at the DSEI Japan conference held near Tokyo.
It is first sent to one of China's two dozen advanced science universities. They in turn apply for Chinese patents on the technology. After they are acquired, the government distributes the patents to various companies.
One of the most notable recipients is telecommunications giant Huawei Industries, which has been under the microscope of U.S. regulators as the company attempts to role out its 5G technology.
Huawei "is one of the best examples of this problem," Schneider said. It has acquired 56,000 5G and artificial intelligence-related Chinese patents despite spending a pittance on research and development, he added.
"It is very important for us to confront and mitigate this problem," he said.
In addition, a 2014 law passed in China requires any Chinese citizen to cooperate with Chinese intelligence services, he said. There are currently 350,000 Chinese students in U.S. universities, he noted.
"That is unlike anything we have had to deal with in the past," he added. "The scale of the problem is obvious."
Schneider displayed a list compiled by the Office of the Director of National Intelligence detailing the myriad ways China acquires intellectual property.
Besides cyber break-ins and traditional spying using human assets, it employs legal means of acquiring technology such as joint ventures, buying companies outright, partnerships with government research institutions, and hiring foreign experts and bringing them to China to work.
Other underhanded means include setting up front companies that obscure its participation and using its laws to give its own companies advantages and foreign companies disadvantages, the list stated.
Topics: Global Defense Market, International, Infotech, Information Technology, Cybersecurity, Cyber
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Re: Expert Details What China Does After Stealing IP
Having visited Huawei and did business with them for a decade, their R&D is huge, and seeing thousands and thousands of engineers working 10 hours a day 6 days a week to innovate. They not copying us. In fact I have seen the opposite with US companies poaching Key Chinese employees and Chinese companies seeing their IP being used illegally. So it goes both ways, and unless you know the technology and actually been there and worked with Chinese companies, the analysis is baseless. And please read the Snowden reports before thinking only China has a major cyber espionage program. China has drastically increase their IP protection, and most US companies say (US -China business council) say China stealing their technology isn't a top issue. But luckily when America was industrializing and stole massive intellectual property from the UK and Europe doesn't count.. do as we say.. not as we did.. no moral standard and as India is becoming an even bigger threat to stealing technology.
Ryan at 7:06 PM
I am 75, love our space program. Totally distressed to see NASA scientist with mars rover program escaped to China after acting double agent giving all the technical information to Chinese military. Now they sucessfully launch rover to moon on first try, send rocket to mars with no equity sweat and that is just the beginning. Microsoft has opened company for AI in China. Chinese Manager now retires in U.S. and moves to China. Shame on us for not standing in the way and making them pay.
Thomas A Young at 11:52 AM


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Democrats in the US House of Representatives on Tuesday introduced a bill that would provide permanent residency/green cards to millions of immigrants.
House bill H.R. 6 seeks to provide under the Deferred Action for Childhood Arrivals (DACA), Temporary Protected Status (TPS), and Deferred Enforced Departure (DED) with permanent residency and a pathway to citizenship.
H.R. 6, also title Dream and Promise Act, is sponsored by three members of Congress; Rep. Lucille Roybal-Allard (CA), Rep. Nydia Velazquez (NY), and Rep. Yvette Clarke (NY).
DACA protects immigrants (DREAMers) brought illegally into the US from deportation, while TPS gives temporary protection from deportation to nationals of countries that the Department of Homeland Security has determined to be unsafe after natural disasters or armed conflict. It currently includes nationalities of these 10 countries; El Salvador, Haiti, Honduras, Nepal, Nicaragua, Somalia, Sudan, South Sudan, Syria, and Yemen. DED on the other hand currently only offers protection to Liberian citizens.
The Dream and Promise Act, if passed into law, would give TPS and DED immigrants in immediate pathway to a green card, by first giving recipients conditional permanent resident status for 10 years. Those who got a degree from a recognized university (or completed two years in good standing of a bachelor's or technical program), served for two years in the military, or worked for three years would be eligible to apply for permanent residency immediately.
It is estimated that H.R. 6 would grant about 2.5 million immigrants with a pathway to permanent residency and eventually to citizenship if passed into law. While the bill could pass in the Democrat-controlled House of Representatives, it is unlikely that the Senate - currently controlled by Republicans - would take up the bill in its current form. President Donald Trump, who has threatened to end the DACA and TPS programs as well as limit immigration to the US in general, would also almost certainly not sign the bill.


Emerging device technologies for nano-oscillators have inspired research in the use of oscillators to perform mathematical operations based on non-Boolean computations using the coupling behavior of an oscillator cluster, rather than CMOS logic gates. For example, circuits using coupled oscillators can be created to measure the Degree of Match (DOM) between two vectors. Coupled oscillators synchronize through a range of frequencies, called the locking region, depending on coupling strength. The output behavior of coupled oscillators with a DOM detector in the locking range has been shown to be the Euclidean distance squared, where larger DOM voltages correspond to more similar vectors. The convolution of two vectors can be calculated using three DOM oscillator clusters based on the algebraic expansion of Euclidean distance squared.
Because the nano-oscillator devices have not matured enough to build large systems, it is important to design models of coupled oscillator behavior. Modeling oscillators is required across a hierarchy consisting of device models, circuit and logic models, and system models in order to support the entire scope of design abstractions. Device models are created by trial and error matching of the model to measured data. The system level model is then created by a polynomial fit to the output of a SPICE simulation of coupled oscillator circuits built using the circuit model. The result is a closed-form system level mathematical model usable in MATLAB and C++.
This thesis presents a study of three models that span the discussed hierarchy: one STO model created based on two different circuit models with different detector characteristics, a VO2 oscillator model, and a generic parameterized model used to evaluate variations in oscillator parameters. All versions were tested at each level of the hierarchy and the results compared to control values to verify the models. The device level model was compared to the empirical data. The circuit level models were used to calculate DOM and convolution; these calculations were compared to MATLAB calculations. The system level models were tested in an image processing pipeline (IPP) and the accuracy of these models was compared to conventional floating-point calculations.


I saw a post on Confessions of a Cookbook Queen's site and was instantly reminded of this my mother-in-law's Caramel Marshmallows. This is an old school recipe and were put out every Christmas. They are probably my second favorite Christmas treat, right after Christmas Wreaths.
Unfortunately, they are so good that they don't generally last until Christmas. Just one or two sneaked in and all of a sudden you have to make a fresh batch. They are easy to make, just have all your ingredients ready to go, your parchment lined cookie prepped and space in the freezer to put them (to set).
My favourite time to eat caramel marshmallows are when they've come right out of the freezer. I eat all the caramel and crunchy outside, leaving a frozen nugget of marshmallow. Then eat the marshmallow. No wasting!
In a double boiler, have an inch or so of water simmering.
Line a cookie sheet with parchment paper and have room in the freezer to set the loaded tray.
Place the caramels and butter in a microwave safe bowl and nuke for 30 seconds at a time. Mixing as best as you can each time.
After about a minute, add a bit of water and nuke again.
Keep adding water until the mixture is thin enough to coat the marshmallows.
Drop marshmallow in and use fork to roll around and coat.
Drop into a bowl of Special K that has been lightly crushed, add some sprinkles if desired, and cover the marshmallow, pressing gently into the marshmallow to coat.
As the caramels melt, it will seem super goopy and that it will never become smooth. Keep whisking and adding water about a teaspoon at a time and it will quickly become the right consistency!


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text:
module Meetupinator
  # doco
  class App
    def self.version
      'meetupinator v' + Meetupinator::VERSION
    end

    def self.retrieve_events(args = {})
      new.retrieve_events(args)
    end

    def self.format(args = {})
      new.format(args)
    end

    def retrieve_events(args)
      init_retrieve(args)
      retrieve_meetup_events(args)
      retrieve_internal_events(args)
    end

    def retrieve_meetup_events(args)
      group_names = @input_file_reader.get_group_names args[:input]
      events = @event_finder.extract_events(group_names, @api, args[:weeks])
      @event_list_file_writer.write events, args[:output]
    end

    def retrieve_internal_events(args)
      events = @input_file_reader.get_interal_events args[:input]
      @event_list_file_writer.write_internal_events events, args[:output]
    end

    def init_retrieve(args)
      @api = Meetupinator::MeetupAPI.new(args[:meetup_api_key])
      @input_file_reader = Meetupinator::InputFileReader.new
      @event_finder = Meetupinator::EventFinder.new
      @event_list_file_writer = Meetupinator::EventListFileWriter.new
    end

    def format(args)
      reader = Meetupinator::EventListFileReader.new
      formatter = Meetupinator::Formatter.new

      events = reader.read(args[:input])
      formatter.format(events, args[:template], args[:output])
    end
  end
end

redpajama_set_name: RedPajamaGithub


Last Call for Nominations – Year End Awards | West Ottawa Ringette Association - Go Wild!
The time has come to put forth your nominations before this Monday, March 7th!
a write up that explains why the nominee is being nominated – how they meet the criteria or spirit of the award.
This is not intended to be an onerous process, however, if there are multiple nominees then enough information should be provided to allow the Board to fully understand the reason and merit of the nomination.
The criteria for the awards is available on the website.
← 2016 MY Ringette Camp Registration is now open!


text:
The Wii U is a game console developed by Nintendo in 2012 and is considered to be the first 8th generation game console, competing with the Xbox One and Playstation 4. As the successor to the Wii, the Wii U is the first Nintendo console to support HD graphics and it's primary controller is the Wii U GamePad, which features an embedded touchscreen, directional buttons, analog sticks and action buttons. The screen on the GamePad can be used either as a supplement to the main screen of a TV set, or be used in place of the TV screen for supported games. The Wii U is also backward compatible with all Wii software and accessories, supporting any combination of GamePad, Wii Remote, Nunchuk, Balance Board, Nintendo's Classic Controller or Wii U Pro Controller. Despite the backward compatibility, the innovative GamePad, improvements in online functionality over the Wii and it's relative affordability, the Wii U received slow consumer adoption due to the GamePad's short battery life, issues with the user interface, a weak lineup of launch titles, limited third-party support and poor marketing of the console. Production of the Wii U officially ended in January of 2017.
Retrieved from "https://rarewiki.com/w/index.php?title=Wii_U&oldid=29877"


After downloading you should erase the translation part and add your own language translation to make a memory game!
This downloadable handout is best suited for elementary school students at Elementary (A1) level. It is a valuable material for strengthening your groups' Reading skills.


Your chance to catch Halestorm is coming up fast. They will be bringing the "Halloween Scream Tour" to the Mayo Civic Center in Rochester, MN on Tuesday, September 26th.
Widely acknowledged as one of modern hard rock's most explosive live acts, Grammy Award winning band Halestorm recently released their third studio album INTO THE WILD LIFE. Recorded last year in Nashville with GRAMMY-nominated producer Jay Joyce at the helm, "INTO THE WILD LIFE" is highlighted by the historic new single, "Apocalyptic". The track recently ascended to the top at Active Rock radio outlets nationwide in an astounding 9 weeks – the first time a female-fronted band has scored #1 hits at the format three times as well as the first to have #1 songs from two consecutive albums. Furthermore, "Apocalyptic"has hit #1 at Mainstream Rock radio, marking Halestorm as the fourth-ever-female-led act to top the format more than once.
Joining on this tour stop will be Starset and New Years Day.


text:
Watch Chris Hemsworth Brutally Axe His Trainer With A Kick To The Face
Sonny Bill Williams' New Training Regime Is The Reason He Is The GOAT
How Former Rugby Star Thom Evans Turned His Misfortunes Into Success
Celeb Trainer Ben Bruno Shares Warning About "Instagram Exercises"
How To Get Your Body Rugby Ready
Here's Why Some Men Are Taking Viagra Before A Workout
Arnie Is Back Pumping Iron, Only 6 Months Post-Surgery
4 Sexual Health Benefits Of Barre Workouts
Grooming 101 With The Gladiator Himself, Russell Crowe
Chase Serious Back Gains With Nick Jonas' Cheeky Instagram Workout
The One Thing That Threw Rugby Great Dan Carter Off His Game
The Chest Workout That Could Score You A Date With A Pop-Princess
In Case You Missed It, Mark Wahlberg Is Still Jacked, And Here's How
Want To Save The World? Fitness Could Be The Key
Sonny Bill Williams Shares Creative Dad Workout Ahead Of Boxing Return
No children were harmed in the making of this session - by Scott Henderson
Sonny Bill Williams is an incredible athlete, having represented New Zealand in both rugby league and rugby union, as well as a successful boxing career. He's an absolute weapon both on the field and off, with the current All Black welcoming his third child in June of this year.
And in an announcement made earlier this week, Williams will be making his return to the boxing ring on December 1 in Sydney, only one week after wrapping up the All Black's European tour, in a charity match against Australian Stu Laundy (yep, the guy who won Sophie Monk's heart on the Bachelorette last year). If you think that the fight seems incredibly one-sided, you're not alone, with All Blacks coach Steve Hansen weighing in.
stulaundy44 View Profile
625 likes - View Post on Instagram
Here's a worthwhile post people
"Look, from my understanding I think it's a very, very low-key fight and it's about raising money for the homeless in Sydney and for the Auckland City Mission," Hansen said to news.com.au.
Laundy is a publican by trade, and faces a huge challenge in taking on Williams, even if it is for a good cause and a laugh. This will be his first fight, and he's taking on a veteran still at peak fitness.
"Sonny has a fair few kilos on me and a fair bit of ability as well, but other than that it's nothing impossible," said a confident Laundy. I'm not scared of anything. Honestly, I'm not scared at all. Really I'm not. Sonny is a beast, but beasts can be taken down."
"I've been throwing drunk guys out of pubs for 30 years. Sure some of them are drunk, but some of them are monsters."
sonnybillwilliams View Profile
I'm back in the ring Dec 1st -Fighting for those who happen to be a bit down on their luck and are homeless in Aotearoa and Australia. It's an honour to be part of this special event ❤
In preparation for his boxing return, and in promotion for the fight, Williams took to social media to share a workout video with a twist, featuring his young kids as motivators, training partners and even weights. Talk about utility players.
#33ReasonsWHY 🏉
The clip features Sonny Bill in his home, waking up to a morning training session that includes weighted lunges, weighted squats, sit-ups to feed his daughter, and a little bit of unwanted agility work thanks to a wayward scooter.
#InMyFeelingsChallenge - parental edition (we promise no children were neglected while making this video) 😂 @lanamarieewilli
Williams funny vid is the latest in a variety of posts from high-profile dads, who refuse to let their fitness and health slip as a result of sleepless nights and parent duties. Australia's own Sam Wood has also posted workouts that incorporate his newborn as some extra resistance when training.
MMA Training
Rugby Training
Scott is the Editor of Men's Health Australia, where he oversees all editorial content of the country's largest men's magazine. As a fitness addict, adventure sport lover, and passionate story-teller, Henderson is committed to living the Men's Health brand.


you will brighten the lives of others.
We are rising toward grace.
in the light of awareness.
and clouds not so dark.
Seed your life with dreams.
Dig out that which no longer serves.
Is the universe friendly? For thousands of years, people have wondered about the essential nature of reality. Today, thanks to the research of thousands of scientists, we have data to answer this question. And the answer is so radical as to be astonishing. The universe is friendly and becoming friendlier. The universe supports us in billions of ways. Furthermore, the universe has a direction toward greater complexity, cooperation, consciousness and compassion. In short, the universe is becoming ever more friendly. When you awaken to the friendliness of the universe, you awaken to grace. A friendly universe is an extraordinary claim, which requires extraordinary evidence. For evidence, read on . . .
Like electricity, grace has power in it. The more you know how your mind evolved, develops, and functions, the better you are able to rewire your brain to experience more grace and create a more beautiful life. With this knowledge, you can effortlessly harness the power of grace to transform obstacles into opportunities. Grace is the radiant happiness that overflows to others.
You are the artist, your thoughts are the paints that color your emotions, and your actions are the brushstrokes that create your world. This web site offers inspiration to help you create your most beautiful masterpiece: your magnificent life.
What is the most beautiful experience you can create?
Envision your most beautiful life. Who is with you? What do you see, hear, smell, taste and feel? What are you doing?


text:
Q: When to use redirection to stderr in shell scripts I know that well-behaved utilities like grep output "normal" messages to stdout, and error messages to stderr.
$ grep '^foo' file1 file2
file1:foo
grep: file2: No such file or directory

When I'm writing shell scripts myself I often find it hard to decide what output and which messages I should present on stderr, or if I should bother at all.
I'd like to know about good practice: When is redirecting some message to stderr called for and reasonable, and when not?
"It depends", sure, but do you have some insights that would help me make these decisions?
In order to make this subjective question fit the format, I would like to encourage answers that address the "why", and are informed by experience and if possible backed by facts.

A: I generally write everything that relates to the application operation to stderr, stdout is reserved for data.
Imagine an application like cat. When you use it to read input and pass it to another application (via a pipe), you don't want the output to be littered with status messages.
Everything that might be interesting to another application or a postprocessor to my application is going to stdout, everything that only relates to the interna of my application is going to stderr.

A:
When I'm writing shell scripts myself I often find it hard to decide
  what output and which messages I should present on stderr, or if I
  should bother at all.

Silence is golden. Output nothing if everything is fine.

I'd like to know about good practice: When is redirecting some message to
  stderr called for and reasonable, and when not?

The easiest way to separate stderr from stdout : just imagine all your scripts output will be redirected to another command via pipe. In that case you should keep all notifications in stderr, as such unexpected information in stdout may break the pipe sequence.
Also sometimes in pipes like this one:
command1 | while read line ; do command2 ; done | command3

you need pass something from command2 to users output. The easiest way without temporary files is stderr.

A: My personal preference is to send error messages and exceptions to stderr and informational messages to stdout.  IMO, stderr is for any exceptions and hence, my convention.


The most amorphous and difficult to trace form of thematic correspondence is the thematic echo, where certain words or short phrases used in one text appear in another. These resonances are particularly difficult to place when multiple sources employ the theme or when the text using these echoes employs different themes in close proximity. As an example of multiple possible sources using one theme, consider Romans 10:7, which reads "or 'Who will descend into the abyss?'" One could argue that here Paul was using Deuteronomy 30:13 ("Who will cross to the other side of the sea for us and get it for us?"), Baruch 3:30 ("Who has gone over the sea, and found her, and will buy her for pure gold?"), or the Wisdom of Ben Sira 24:5 ("Alone I compassed the vault of heaven and traversed the depths of the abyss.").
While occasionally the surrounding context or verbal dissimilarities with one of the possible sources will delimit these types of citation, often times this is not the case. The prayer in 1 Clement 59-60 provides an example of when a text uses different themes in close proximity to one another. Over the years scholars have argued for references to no fewer than twenty-six different texts in this prayer, although each possible use involves little more than thematic resonance. In such cases, it makes less sense to try and locate a particular source text than it does to simply attribute the reference to thematic similarity. Thematic echoes mark the end of the spectrum of thematic correspondence, where words or ideas are recognizable but contain little more than reminiscence.
 Richard B. Hays, The Conversion of Imagination: Paul as Interpreter of Israel's Scripture (Grand Rapids: Eerdmans, 2005), 166. Hollander, 89-91.  Hays, Conversion, 164-5. Zahn, "Identifying," 344. Benjamin D. Sommer, "Exegesis, Allusion, and Intertextuality in the Hebrew Bible: A Response to Lyle Eslinger," VT 46 (1996): 479-89, esp. 486.  New Revised Standard Version: Harper Collins Study Bible Revised Edition (ed. H.W. Attridge, San Francisco: Harper Collins, 2006). ἤ , Τίς καταβήσεται εἰς τὴν ἄβυσσον; (NA 28).  New English Translation of the Septuagint (ed. B. Wright and A. Pietersma, Oxford: Oxford University Press, 2009). τίς διαπεράσει ἡμῖν εἰς τὸ πέραν τῆς θαλάσσης καὶ λήμψεται ἡμῖν αὐτήν (LXX).  NRSV. τίς διέβη πέραν τῆς θαλάσσης καὶ εὗρεν αὐτὴν καὶ οἴσει αὐτὴν χρυσίου ἐκλεκτοῦ (LXX).  NRSV. γῦρον οὐρανοῦ ἐκύκλωσα μόνη καὶ ἐν βάθει ἀβύσσων περιεπάτησα (LXX).  Gregory and Tuckett's discussion of thematic echo in the context of determining which Synoptic Gospel stands behind a reference is helpful here, as is their summary of the methodological battle between Edouard Massaux, Helmut Koester, and W.D. Kohler. Gregory and Tuckett, 69-75. See Massaux, Koester, and Kohler.  Prahlow, Appendix A.