Abstract
Adaptive modulation can optimize the spectrum efficiency and system performance with the channel state information achieved by the long-range channel prediction. To avoid re-estimating channel correlation function as the channel stationarity varies and to track the channel adaptively, LMS (Least-Mean-Square) based long-range channel prediction is discussed in the existing literature, but it needs long observation interval to reach the convergence. Given that all OFDM (Orthogonal Frequency Division Multiplexing) subcarriers have the identical time-domain correlation and stationarity during the same time interval, this paper proposed a 2-D LMS based predictor which updates the filter weights in both time and frequency domain. The proposed scheme can effectively decrease the observation intervals and significantly speed up the convergence than the conventional LMS and Parallel LMS (PLMS). Complexity analysis and simulation results prove that the proposed scheme can improve the BER (Bit Error Rate) performance and spectrum efficiency with negligible complexity increase.
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