Capacity-Region-Achieving Sparse Regression Codes for MIMO Multiple-Access Channels

2026-04-13

AI Summary

This work addresses the challenge of efficiently achieving the capacity region of the multiple-input multiple-output multiple-access channel (MIMO-MAC) by proposing a novel coding scheme based on sparse regression codes and random semi-unitary dictionary matrices. At the receiver, reliable parallel interference cancellation is realized through the multi-access orthogonal approximate message passing (MA-OAMP) algorithm. By jointly optimizing power allocation and position-modulated signal design, the study establishes, for the first time, an optimal coding principle tailored to MA-OAMP receivers. The proposed scheme theoretically attains the sum capacity of the MIMO-MAC and, when combined with time-sharing strategies, fully achieves the entire capacity region, thereby surpassing the performance limitations of conventional multiuser coding approaches.

Technology Category

• Machine Learning: Optimization
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

This paper proposes a coding framework for capacity-region-achieving sparse regression (SR) codes over MIMO multiple-access channels (MIMO-MAC), where a single SR code is used for each user at the transmitter. With random semi-unitary dictionary matrices applied for encoding, multiple-access OAMP (MA-OAMP) enables reliable parallel interference cancellation (PIC) at the receiver. Theoretically, an optimal coding principle with the MA-OAMP receiver, which achieves the sum capacity and, in combination with time sharing, achieves the entire capacity region, is established as the guiding principle for designing capacity-region-achieving codes. Accordingly, a coding scheme for capacity-region-achieving SR codes is proposed via proper power allocation over the position-modulated signals.

Problem

• sparse regression codes
• MIMO multiple-access channels
• capacity region
• coding framework

Innovation

• Sparse Regression Codes
• MIMO-MAC
• MA-OAMP
• Capacity Region
• Parallel Interference Cancellation