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Training Sequence Reduction for the Least Mean SquareBlind Joint Maximum Likelihood Sequence Estimation Cochannel Interference Cancellation Algorithm in OFDM Systems
Zhenyu ZHOU Takuro SATO
Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Vol.E94A
No.5
pp.11731183 Publication Date: 2011/05/01
Online ISSN: 17451337
DOI: 10.1587/transfun.E94.A.1173
Print ISSN: 09168508 Type of Manuscript: PAPER Category: Digital Signal Processing Keyword: LMSBJMLSE, training sequence reduction, interference cancellation, subcarrier identification and interpolation, OFDM, receiver diversity,
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Summary:
Due to the reuse factor reduction, the attendant increase in cochannel interference (CCI) becomes the limiting factor in the performance of the orthogonal frequency division multiplexing (OFDM) based cellular systems. In the previous work, we proposed the least mean squareblind joint maximum likelihood sequence estimation (LMSBJMLSE) algorithm, which is effective for CCI cancellation in OFDM systems with only one receive antenna. However, LMSBJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In this paper, we propose a subcarrier identification and interpolation algorithm, in which the subcarriers are divided into groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the meansquare error (MSE) produced by LMS and the meansquare deviation (MSD) of the desired channel estimate. The identified poor channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm can reduce the required training sequence dramatically for both the cases of single interference and dual interference. We also generalize LMSBJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement.

