Deep Learning for Uplink Spectral Efficiency in Cell-Free Massive MIMO Systems

TL;DR

This paper proposes a deep neural network approach to optimize uplink spectral efficiency in cell-free massive MIMO systems, achieving near-optimal performance with significantly reduced computational complexity.

Contribution

Introduces a DNN-based method for maximizing proportional fairness in spectral efficiency, outperforming traditional optimization in speed while maintaining high accuracy.

Findings

DNN achieves ~1% loss in sum rate compared to iterative algorithms.

Proposed DNN significantly reduces computational complexity.

Method is suitable for real-time processing in CF massive MIMO systems.

Abstract

In this paper, we introduce a Deep Neural Network (DNN) to maximize the Proportional Fairness (PF) of the Spectral Efficiency (SE) of uplinks in Cell-Free (CF) massive Multiple-Input Multiple-Output (MIMO) systems. The problem of maximizing the PF of the SE is a non-convex optimization problem in the design variables. We will develop a DNN which takes pilot sequences and large-scale fading coefficients of the users as inputs and produces the outputs of optimal transmit powers. By consisting of densely residual connections between layers, the proposed DNN can efficiently exploit the hierarchical features of the input and motivates the feed-forward nature of DNN architecture. Experimental results showed that, compared to the conventional iterative optimization algorithm, the proposed DNN has excessively lower computational complexity with the trade-off of approximately only 1% loss in the sum rate and the fairness performance. This demonstrated that our proposed DNN is reasonably suitable for real-time signal processing in CF massive MIMO systems.