A Novel Low Complexity Faster-Than-Nyquist Signaling Detector Based on the Primal-Dual Predictor-Corrector Interior Point Method

Abstract

Faster-than-Nyquist signaling (FTNS) is a promising non-orthogonal physical layer transmission technique to improve the spectral efficiency of next-generation communication systems but at the expense of increased transciever complexity to remove the artificially introduced intersymbol-interference (ISI). In this paper, we investigate the detection problem of FTNS and propose a low-complexity sub-optimal detection algorithm, stemming in operations research, that is based on convex relaxation, primal-dual predictor-corrector interior point method, and quantization. The proposed convex quadratic relax-and-quantize sequence estimation (CQRAQSE) algorithm is suitable for low rates FTNS and demonstrates an excellent trade-off between the detection performance and computational effort.