Codeword-Segmentation Rate-Splitting Multiple Access and Evaluation under Suboptimal Decoding

2025-06-20

AI Summary

In downlink rate-splitting multiple access (RSMA), the conventional message-splitting-then-coding cascade incurs high encoding/decoding complexity, substantial signaling overhead, and cumbersome hybrid automatic repeat request (HARQ) retransmission mechanisms. Method: This paper proposes codeword-splitting RSMA (CS-RSMA), which performs public/private stream splitting and joint precoding directly on user codewords *after* channel coding—departing from the traditional “split-then-code” paradigm. Contribution/Results: We develop a novel performance analysis framework tailored to finite-alphabet inputs and suboptimal decoding, explicitly incorporating practical receiver complexity constraints. Theoretical analysis and link-level simulations demonstrate that CS-RSMA achieves higher sum rates than classical RSMA while maintaining comparable max-min fairness. Moreover, it significantly simplifies encoder/decoder architectures, streamlines control signaling, and reduces HARQ retransmission complexity—thereby comprehensively lowering overall system implementation complexity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML
• Multiagent Systems: Coordination and Collaboration

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Rate-Splitting Multiple Access (RSMA) has been recognized as a promising multiple access technique. We propose a novel architecture for downlink RSMA, namely Codeword-Segmentation RSMA (CS-RSMA). Different from conventional RSMA which splits users'messages into common and private parts before encoding, CS-RSMA encodes the users'messages directly, segments the codewords into common and private parts, and transmits the codeword segments using common and private streams. In addition to the principle of CS-RSMA, a novel performance analysis framework is proposed. This framework utilizes a recent discovery in mismatched decoding under finite-alphabet input and interference, and can better capture the receiver's complexity limits. Precoder optimization under finite alphabets and suboptimal decoders for conventional RSMA and CS-RSMA to maximize the Sum-Rate (SR) and the Max-Min Fairness (MMF) is also addressed. The numerical results reveal the theoretical performance of conventional RSMA and CS-RSMA. We observe that CS-RSMA leads to better performance than conventional RSMA in SR, and similar performance in MMF. Furthermore, a physical-layer implementation of CS-RSMA is proposed and evaluated through link-level simulations. Aside performance benefits, we also demonstrate that CS-RSMA brings significant benefits on the encoding/decoding, control signaling, and retransmission process compared to conventional RSMA.

Problem

• Proposes Codeword-Segmentation RSMA for downlink transmission
• Analyzes performance under finite-alphabet input and interference
• Optimizes precoders for Sum-Rate and Max-Min Fairness

Innovation

• CS-RSMA segments codewords into common and private parts
• Novel performance analysis framework for suboptimal decoding
• Optimized precoder for Sum-Rate and Max-Min Fairness