Model-based Deep Learning for Wireless Resource Allocation in RSMA Communications Systems

TL;DR

This paper introduces a model-based deep learning approach using deep unfolding for efficient and robust resource allocation in RSMA systems, outperforming traditional methods in speed and data efficiency.

Contribution

It proposes a fractional programming-based deep unfolding neural network that improves RSMA resource management with lower complexity and better generalization than existing data-driven methods.

Findings

Achieves similar performance to traditional algorithms with reduced computational complexity.

Requires less training data and exhibits higher resilience to out-of-distribution data.

Demonstrates effectiveness through extensive simulations.

Abstract

Rate-splitting multiple access (RSMA) has been proven as an effective communication scheme for 5G and beyond. However, current approaches to RSMA resource management require complicated iterative algorithms, which cannot meet the stringent latency requirement by users with limited resources. Recently, data-driven methods are explored to alleviate this issue. However, they suffer from poor generalizability and scarce training data to achieve satisfactory performance. In this paper, we propose a fractional programming (FP) based deep unfolding (DU) approach to address resource allocation problem for a weighted sum rate optimization in RSMA. By carefully designing the penalty function, we couple the variable update with projected gradient descent algorithm (PGD). Following the structure of PGD, we embed a few learnable parameters in each layer of the DU network. Through extensive simulation, we have shown that the proposed model-based neural networks can yield similar results compared to the traditional optimization algorithm for RSMA resource management but with much lower computational complexity, less training data, and higher resilience to out-of-distribution (OOD) data.