Maximizing the Sum Rate in Cellular Networks Using Multi-Convex Optimization

TL;DR

This paper introduces a novel multi-convex optimization algorithm to maximize sum rate in interference-limited cellular networks, effectively handling non-convex transmit and receive filter design problems.

Contribution

It formulates the sum rate maximization as a multi-convex problem with auxiliary variables and proposes an iterative alternating optimization algorithm.

Findings

Effective sum rate maximization in cellular networks.

Applicable to scenarios with or without relays.

Demonstrates broad applicability to wireless systems.

Abstract

In this paper, we propose a novel algorithm to maximize the sum rate in interference-limited scenarios where each user decodes its own message with the presence of unknown interferences and noise considering the signal-to-interference-plus-noise-ratio. It is known that the problem of adapting the transmit and receive filters of the users to maximize the sum rate with a sum transmit power constraint is non-convex. Our novel approach is to formulate the sum rate maximization problem as an equivalent multi-convex optimization problem by adding two sets of auxiliary variables. An iterative algorithm which alternatingly adjusts the system variables and the auxiliary variables is proposed to solve the multi-convex optimization problem. The proposed algorithm is applied to a downlink cellular scenario consisting of several cells each of which contains a base station serving several mobile stations. We examine the two cases, with or without several half-duplex amplify-and-forward relays assisting the transmission. A sum power constraint at the base stations and a sum power constraint at the relays are assumed. Finally, we show that the proposed multi-convex formulation of the sum rate maximization problem is applicable to many other wireless systems in which the estimated data symbols are multi-affine functions of the system variables.