Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access

TL;DR

This paper introduces a model-based deep learning receiver for Rate-Splitting Multiple Access that improves interference management in wireless systems, especially under imperfect channel information, by combining traditional SIC with deep learning techniques.

Contribution

It proposes a novel MBDL-based RSMA receiver that enhances robustness and performance over traditional SIC, particularly with imperfect CSIR, using a data-driven approach.

Findings

MBDL receiver outperforms SIC with imperfect CSIR.

Significant reduction in Symbol Error Rate (SER).

Improved throughput in link-level simulations.

Abstract

Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.