Fractional Programming for Stochastic Precoding over Generalized Fading Channels

TL;DR

This paper introduces an efficient stochastic precoding algorithm for MIMO networks that relies only on the first two moments of fading channels, using a novel lower bound approach to optimize long-term sum rates.

Contribution

It proposes a new fractional programming-based method that works with generalized fading channels without assuming specific distributions, improving efficiency and performance.

Findings

Outperforms benchmark methods in Gaussian and non-Gaussian channels

Efficient closed-form iterative solution for the approximate problem

Enhanced scalability for large-scale MIMO systems

Abstract

This paper seeks an efficient algorithm for stochastic precoding to maximize the long-term average weighted sum rates throughout a multiple-input multiple-output (MIMO) network. Unlike many existing works that assume a particular probability distribution model for fading channels (which is typically Gaussian), our approach merely relies on the first and second moments of fading channels. For the stochastic precoding problem, a naive idea is to directly apply the fractional programming (FP) method to the data rate inside the expectation; it does not work well because the auxiliary variables introduced by FP are then difficult to decide. To address the above issue, we propose using a lower bound to approximate the expectation of data rate. This lower bound stems from a nontrivial use of the matrix FP, and outperforms the existing lower bounds in that it accounts for generalized fading channels whose first and second moments are known. The resulting approximate problem can be efficiently solved in closed form in an iterative fashion. Furthermore, for large-scale MIMO, we improve the efficiency of the proposed algorithm by eliminating the large matrix inverse. Simulations show that the proposed stochastic precoding method outperforms the benchmark methods in both Gaussian and non-Gaussian fading channel cases.