Optimum Transmission Through the Multiple-Antenna Gaussian Multiple Access Channel

TL;DR

This paper investigates optimal transmission strategies in Gaussian multiple access channels with multiple antennas and fading, developing a unified framework and algorithm for maximizing various rate objectives under different constraints.

Contribution

It introduces a novel matrix variable approach to jointly optimize time-sharing, decoding order, and input covariance, extending results to nonconvex problems in GMACs.

Findings

Derived necessary and sufficient optimality conditions.

Developed a convergent algorithm for GMAC optimization.

Extended analysis to general, nonconvex rate objectives.

Abstract

This paper studies the optimal points in the capacity region of Gaussian multiple access channels (GMACs) with constant fading, multiple antennas and various power constraints. The points of interest maximize general rate objectives that arise in practical communication scenarios. Achieving these points constitutes the task of jointly optimizing the time-sharing parameters, the input covariance matrices and the order of decoding used by the successive interference cancellation receiver. To approach this problem, Carath\'eodory's theorem is invoked to represent time-sharing and decoding orders jointly as a finite-dimensional matrix variable. This variable enables us to use variational inequalities to extend results pertaining to problems with linear rate objectives to more general, potentially nonconvex, problems, and to obtain a necessary and sufficient condition for the optimality of the transmission parameters in a wide range of problems. Using the insights gained from this condition, we develop and analyze the convergence of an algorithm for solving, otherwise daunting, GMAC-based optimization problems.