Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions

TL;DR

This paper introduces a computational algorithm for constructing Pareto-optimal precoders under per-antenna power constraints in MIMO systems, effectively handling non-negligible noise with cubic complexity similar to Zero-Forcing.

Contribution

It presents a novel algorithm that constructs SINR multiobjective Pareto-optimal precoders under per-antenna power constraints, adaptable to noise conditions.

Findings

Algorithm achieves Pareto-optimal precoding with cubic complexity.

Parameterization allows skewing user throughput priorities.

Effective under non-negligible noise conditions.

Abstract

Precoding matrix construction is a key element of the wireless signal processing using the multiple-input and multiple-output model. It is established that the problem of global throughput optimization under per-antenna power constraints belongs, in general, to the class of monotonic optimization problems, and is unsolvable in real-time. The most widely used real-time baseline is the suboptimal solution of Zero-Forcing, which achieves a cubic complexity by discarding the background noise coefficients. This baseline, however, is not readily adapted to per-antenna power constraints, and performs poorly if background noise coefficients are not negligible. In this paper, we are going to present a computational algorithm which constructs a precoder that is SINR multiobjective Pareto-optimal under per-antenna power constraints - with a complexity that differs from that of Zero-Forcing only by a constant factor. The algorithm has a set of input parameters, changing which skews the importance of particular user throughputs: these parameters make up an efficient parameterization of the entire Pareto boundary.