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.
This paper presents a novel architecture for downlink Rate-Splitting Multiple Access (RSMA) called Codeword-Segmentation RSMA (CS-RSMA), aiming to reduce implementation challenges associated with conventional RSMA. Traditional RSMA splits users' messages into common and private parts prior to encoding; in contrast, CS-RSMA encodes users' messages directly and segments the codewords into common and private streams. A new performance analysis framework is developed, utilizing mismatched decoding under finite-alphabet inputs, to reflect receiver complexity limits more accurately. The paper addresses precoder optimization for both CS-RSMA and conventional RSMA to maximize the Sum-Rate (SR) and Max-Min Fairness (MMF). Theoretical and numerical evaluations suggest that CS-RSMA outperforms conventional RSMA in terms of SR, while maintaining similar performance in terms of MMF. Additional benefits of CS-RSMA include reduced encoding/decoding complexity, lower signaling overhead, and simplified retransmission mechanisms, demonstrating advantages in practical applications over traditional RSMA methodologies.
This paper employs the following methods:
- Rate-Splitting Multiple Access (RSMA)
- Codeword-Segmentation RSMA (CS-RSMA)
- Finite-Alphabet Gaussian Channel under Interference (FAGCI)
- Mismatched Decoding
- Precoding Optimization
The following datasets were used in this research:
- Sum-Rate (SR)
- Max-Min Fairness (MMF)
- Generalized Mutual Information (GMI)
- CS-RSMA leads to better performance than conventional RSMA in Sum-Rate (SR)
- CS-RSMA has similar performance to conventional RSMA in Max-Min Fairness (MMF)
- Significant reduction in encoding/decoding complexity and signaling overhead with CS-RSMA
- Number of GPUs: None specified
- GPU Type: None specified
- Compute Requirements: None specified