11 research outputs found
Symbol Error Rate Performance of Box-relaxation Decoders in Massive MIMO
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 iid 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 the zero-forcing decoder. For BPSK signals the SER
performance of the BRO is within 3dB 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
Large System Analysis of Box-Relaxation in Correlated Massive MIMO Systems Under Imperfect CSI (Extended Version)
In this paper, we study the mean square error (MSE) and the bit error rate
(BER) performance of the box-relaxation decoder in massive
multiple-input-multiple-output (MIMO) systems under the assumptions of
imperfect channel state information (CSI) and receive-side channel correlation.
Our analysis assumes that the number of transmit and receive antennas (,and
) grow simultaneously large while their ratio remains fixed. For simplicity
of the analysis, we consider binary phase shift keying (BPSK) modulated
signals. The asymptotic approximations of the MSE and BER enable us to derive
the optimal power allocation scheme under MSE/BER minimization. Numerical
simulations suggest that the asymptotic approximations are accurate even for
small and . They also show the important role of the box constraint in
mitigating the so called double descent phenomenon