Linear Processing and Sum Throughput in the Multiuser MIMO Downlink

TL;DR

This paper investigates linear precoding and decoding in multiuser MIMO downlink systems, proposing MSE-based optimization criteria linked to capacity, and introduces an efficient iterative algorithm for near-optimal sum throughput performance.

Contribution

It introduces a novel MSE-based optimization framework for joint transmit-receive design in multiuser MIMO downlink, including a simplified scalar problem and an efficient iterative solution.

Findings

The PDetMSE criterion aligns with maximum sum throughput.

The PMSE algorithm achieves near-optimal sum rates.

Simulations show competitive performance against channel capacity.

Abstract

We consider linear precoding and decoding in the downlink of a multiuser multiple-input, multiple-output (MIMO) system, wherein each user may receive more than one data stream. We propose several mean squared error (MSE) based criteria for joint transmit-receive optimization and establish a series of relationships linking these criteria to the signal-to-interference-plus-noise ratios of individual data streams and the information theoretic channel capacity under linear minimum MSE decoding. In particular, we show that achieving the maximum sum throughput is equivalent to minimizing the product of MSE matrix determinants (PDetMSE). Since the PDetMSE minimization problem does not admit a computationally efficient solution, a simplified scalar version of the problem is considered that minimizes the product of mean squared errors (PMSE). An iterative algorithm is proposed to solve the PMSE problem, and is shown to provide near-optimal performance with greatly reduced computational complexity. Our simulations compare the achievable sum rates under linear precoding strategies to the sum capacity for the broadcast channel.