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Published July 1, 2018 | Submitted
Journal Article Open

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

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.

Additional Information

© 2018 IEEE. Manuscript received August 27, 2017; revised January 15, 2018 and March 29, 2018; accepted April 15, 2018. Date of publication April 30, 2018; date of current version May 29, 2018. The work of W. Xu was supported in part by Simons Foundation 318608 and in part by NSF DMS-1418737. The work of B. Hassibi was supported in part by the National Science Foundation under Grants CNS-0932428, CCF-1018927, CCF-1423663, and CCF-1409204; in part by the Office of Naval Research under the MURI Grant N00014-08-0747; in part by a grant from Qualcomm Inc., and in part by King Abdulaziz University.

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