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Symbol Error Rate Performance of Box-Relaxation Decoders in Massive MIMO
Journal article   Peer reviewed

Symbol Error Rate Performance of Box-Relaxation Decoders in Massive MIMO

Christos Thrampoulidis, Weiyu Xu and Babak Hassibi
IEEE transactions on signal processing, Vol.66(13), pp.3377-3392
07/01/2018
DOI: 10.1109/TSP.2018.2831622
url
https://arxiv.org/pdf/1711.11215View
Open Access

Abstract

The maximum-likelihood (ML) decoder for symbol detection in large multiple-input multiple-output wireless communication systems is typically computationally prohibitive. In this paper, we study a popular and practical alternative, namely the box-relaxation optimization (BRO) decoder, which is a natural convex relaxation of the ML. For independent identically distributed real Gaussian channels with additive Gaussian noise, we obtain exact asymptotic expressions for the symbol error rate (SER) of the BRO. The formulas are particularly simple, they yield useful insights, and they allow accurate comparisons to the matched-filter bound (MFB) and to linear decoders, such as zero-forcing and linear minimum mean square error. For binary phase-shift keying signals, the SER performance of the BRO is within 3 dB of the MFB for square systems, and it approaches the MFB as the number of receive antennas grows large compared to the number of transmit antennas. Our analysis further characterizes the empirical density function of the solution of the BRO, and shows that error events for any fixed number of symbols are asymptotically independent. The fundamental tool behind the analysis is the convex Gaussian min-max theorem.
 asymptotic performance Binary phase shift keying convex relaxation Decoding Error analysis Gaussian processes Massive MIMO MIMO communication nonlinear decoders Optimization Receiving antennas Signal to noise ratio

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