M-BCJR Algorithm with Channel Shortening Based on Ungerboeck Observation Model for Faster-than-Nyquist Signaling

摘要

The M-BCJR algorithm based on the Ungerboeck observation model is a recent study to reduce the computational complexity for faster-than-Nyquist(FTN)signaling[1].In this paper,we propose a method that can further reduce the complexity with the approximately same or better bit error rate(BER)performance compared to[1].The information rate(IR)loss for the proposed method is less than 1%compared to the true achievable IR(AIR).The proposed improvement is mainly by introducing channel shortening(CS)before the M-BCJR equalizer.In our proposal,the Ungerboeck M-BCJR algorithm and CS can work together to defeat severe inter-symbol interference(ISI)introduced by FTN signaling.The ISI length for the M-BCJR algorithm with CS is optimized based on the criterion of the IR maximization.For the two cases=0.5 and=0.35,compared to Ungerboeck M-BCJR without CS benchmark[1],the computational complexities of Ungerboeck M-BCJR with CS are reduced by 75%.Moreover,for the case=0.35,the BER performance of Ungerboeck M-BCJR with CS outperforms that of the conventional M-BCJR in[1]at the low signal to noise ratio region.