Training Sequence Reduction for Blind Single Antenna Interference Cancellation Algorithm in MQAM-OFDM Systems

摘要

In orthogonal frequency division multiplexing (OFDM) based cellular systems, co-channel interference (CCI) from adjacent interfering base stations (BSs) operating and co-existing with the desired BS in the same frequency channel would greatly degrade the bit error rate (BER) performance of cell-border users. In our previous work, a blind single antenna interference cancellation (SAIC) algorithm named least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) has been proposed. The proposed LMS-BJMLSE algorithm is blind with respect to interfering signals and neither the training sequence (TS) nor pilot signal from interferers is needed. In this paper, its BER and channel estimation performances are analyzed. We found that due to the estimation bias of LMS, some subcarrier estimates converge fast while others exhibit a slow rate of convergence. It is those slow-converging subcarriers which contribute mostly to BER especially when desired TS is insufficient. A low complexity algorithm is proposed to identify those poor subcarrier estimates. Several algorithms are proposed to decrease the required TS length. Simulation results prove that the best algorithm named nearest neighbor interpolation (NNI) could reduce the required TS length by 80%.