An IP-based mobile system includes at least one mobile node on a wireless communication system. A “mobile node” is sometimes referred to as user equipment, mobile unit, mobile terminal, mobile device, or similar names depending on the nomenclature adopted by particular system providers. The various components on the system may be called different names depending on the nomenclature used on any particular network configuration or communication system.
For instance, “mobile node” or “user equipment” encompasses PC's having cabled (e.g., telephone line (“twisted pair”), Ethernet cable, optical cable, and so on) connectivity to the wireless network, as well as wireless connectivity directly to the cellular network, as can be experienced by various makes and models of mobile terminals (“cell phones”) having various features and functionality, such as Internet access, e-mail, messaging services, and the like. The term “mobile node” also includes a mobile communication unit (e.g., mobile terminal, “smart phones,” nomadic devices such as laptop PCs with wireless connectivity).
User equipment or mobile nodes are receivers of signals in an uplink direction of signals transmitted from an access point, which is called a transmitter in that configuration. Terms, such as transmitter or receiver, are not meant to be restrictively defined, but could include various mobile communication units or transmission devices located on the network. Further, the words “receiver” and “transmitter” may be referred to as “access point” (AP), “basestation,” or “user equipment” depending on which direction the communication is being transmitted and received. For example, an access point AP or a basestation (eNodeB or eNB) is the transmitter and user equipment is the receiver for downlink environments, whereas an access point AP or a basestation (eNodeB or eNB) is the receiver and user equipment is the transmitter for uplink environments.
Accurate channel estimation is integral to maintaining connectivity, achieving good capacity and throughput performance on the uplink communication link supporting transmission from a mobile node (or user equipment) to a base station (or access point) on an LTE wireless system. When the mobile node or user equipment is traveling at high velocities relative to the access point (transmitter), the known channel estimation methods have two major deficiencies that can have a negative impact on system performance. First, the known channel estimation methods (for example, a minimum mean squared error (MMSE) based estimation) prove to be inaccurate as the user equipment (or mobile node) travel at high velocities. Second, the known channel estimation equations are computationally intensive, which unnecessarily consumes system resources, increases system overhead, and increases the time needed to complete the channel estimation. Thus, to realize good capacity/throughput performance at high mobility and/or high channel frequencies, the issues of performance degradation due to channel estimation inaccuracy need to be addressed, as well as the need to reduce the complexity of the channel estimation algorithms.
The minimum mean squared error (MMSE) based channel estimator utilizes the second-order statistics of the channel conditions to minimize the mean-square error of the channel estimates. An underlying assumption is that the time domain channel vector is Gaussian and uncorrelated with the channel noise. Linear MMSE channel estimate is given as follows:ĤLMMSE=RHH(RHH+σN2(XX*)−1)−1ĤLS  (1)
where                RHH=E [HH*] is the frequency domain channel correlation matrix, H is the frequency domain channel response, * denotes conjugate transpose        X is the vector containing known pilot or the known reference symbol (RS) sequence,        σN2 is the variance of the channel noise,        ĤLS=X−1y, is the Least Squares (LS) estimate of the channel where y is the received vector of RS symbols.        
The MMSE estimator yields much better performance than LS channel estimators by themselves, especially under the low SNR scenarios such as low velocity of the user equipment. A major drawback of the MMSE estimator, however, is its high computational complexity because of the increased consumption of system resources and increased system overhead from performing the full MMSE equation.
In equation (1), X denotes the known reference signal (RS) sequence transmitted by the UE. It should be noted that in the LTE standard, sequence hopping and group hopping are allowed for the uplink RS sequence. If sequence/group hopping is enabled, then the above MMSE equation will require that two matrix inverses be performed every slot (0.5 msec). With one RB allocation to any user, the matrix size is 12×12 whereas with all 48 RBs allocation to one user, the matrix size is 576×576. Such real-time matrix inversions are computationally intensive for practical implementation, and this MMSE equation proves to be inaccurate as the user equipment (or mobile node) travel at higher velocities.
It should also be noted that signal-to-noise ratio (SNR) estimation is an essential processing step in the eNodeB receiver in some circumstances, and if there is an under-estimation of the SNR, system performance is degraded. As such, there is a need to accurately estimate the SNR for use in certain channel estimation procedures to enhance system performance and minimize degradation of system parameters.
It should be noted further that channel estimation methods also use interpolation, and the several approaches for interpolation (e.g. linear, bilinear, or quadratic etc.) do not provide a satisfactory result in certain circumstances. These approaches to interpolation are static and the same approach is generally used for all UE mobilities, and as such does not provide the optimum SNR vs. SER performance for certain situations.