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 reduc...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.展开更多
In this paper, we propose a novel idea for improvement performances of the leader M-BCJR algorithm functioning in low complexity. The basic idea consists to localize error instant possibility, and then increase the co...In this paper, we propose a novel idea for improvement performances of the leader M-BCJR algorithm functioning in low complexity. The basic idea consists to localize error instant possibility, and then increase the complexity around this moment. We also propose an easy and important idea for early localisation of erroneous moments. We call this new algorithm Z-MAP. The simulations show that the improvement of performances is significant. The performances of Z-MAP turbo decoding are so close to full MAP-BCJR performances. Furthermore, the complexity is the same that of the M-BCJR. So, Z-MAP is an optimal version of M-BCJR algorithm.展开更多
基金This work was supported by National Natural Science Foundation of China(No.61961014).
文摘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.
文摘In this paper, we propose a novel idea for improvement performances of the leader M-BCJR algorithm functioning in low complexity. The basic idea consists to localize error instant possibility, and then increase the complexity around this moment. We also propose an easy and important idea for early localisation of erroneous moments. We call this new algorithm Z-MAP. The simulations show that the improvement of performances is significant. The performances of Z-MAP turbo decoding are so close to full MAP-BCJR performances. Furthermore, the complexity is the same that of the M-BCJR. So, Z-MAP is an optimal version of M-BCJR algorithm.
