Reduced-Complexity Equalization for Faster-Than-Nyquist Signaling: New Methods Based on Ungerboeck Observation Model.

In this paper, we consider the detection of faster-than-Nyquist (FTN) signaling. By noticing that the whitening filter for FTN signaling cannot be directly derived when the symbol rate exceeds the signal bandwidth, we propose a new reduced-complexity M-algorithm BCJR (M-BCJR) algorithm based on the Ungerboeck observation model. By taking some “future” symbols into account, the proposed algorithm is able to select the $M$ best states in the maximum a posteriori sense. We further simplify the above algorithm by choosing the key path from each possible state, which successfully reduces the complexity while maintaining a good bit error rate performance. Simulation results show that, with the use of the proposed methods, great gains can be obtained in terms of spectral efficiency (up to 186%) or signal-to-noise ratio (up to 4.5 dB) compared with the Nyquist signaling. [ABSTRACT FROM PUBLISHER]