PROJECT TITLE :
Symbol Error Rate Performance of Box-Relaxation Decoders in Massive MIMO - 2018
The utmost-chance (ML) decoder for image detection in giant multiple-input multiple-output wireless communication systems is typically computationally prohibitive. In this Project, we study a widespread and practical various, specifically the box-relaxation optimization (BRO) decoder, that is a natural convex relaxation of the ML. For freelance identically distributed real Gaussian channels with additive Gaussian noise, we have a tendency to obtain exact asymptotic expressions for the symbol error rate (SER) of the BRO. The formulas are notably easy, they yield helpful insights, and they allow correct comparisons to the matched-filter sure (MFB) and to linear decoders, like zero-forcing and linear minimum mean sq. error. For binary section-shift keying signals, the SER performance of the BRO is at intervals three dB of the MFB for sq. systems, and it approaches the MFB as the quantity of receive antennas grows massive compared to the quantity of transmit antennas. Our analysis more characterizes the empirical density perform of the solution of the BRO, and shows that error events for any fastened variety of symbols are asymptotically freelance. The elementary tool behind the analysis is the convex Gaussian min-max theorem.
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