Channel Estimation using Adaptive Filtering for LTE-Advanced
For demand of high data rates, enhanced system capacity and coverage, ITU made proposal for the standardization of next generation wireless communication systems, known as IMT-Advanced. To achieve these targets, a priori knowledge of the channel is required at the transmitter side. In this paper, three adaptive channel estimation techniques: Least Mean Square (LMS), Recursive Least Square (RLS) and Kalman-Filtering Based, are compared in terms of performance and complexity. For performance, Mean Square Error (MSE) and Symbol Error Rate (SER) while for complexity, computational time is measured for variable channel impulse response (CIR) lengths and channel taps. MATLAB Monte-Carlo Simulations are used to evaluate these techniques.
Keywords: LTE-A, Kalman Filtering, LMS, LSE, RLS, LMMSE, DFT-based, DCT-Based, Windowed DFT-Based
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