An Information-Theoretic Efficient Capacity Region for Multi-User Interference Channel

2025-01-25

AI Summary

Characterizing the capacity region of multi-user interference channels remains challenging due to high SIC decoding complexity, reliance on auxiliary random variables, and cumbersome joint typicality arguments. Method: This paper proposes a novel information-theoretic framework based on partial multiple-access channel (partial-MAC) modeling and chain-rule decomposition, integrated with the entropy power inequality (EPI) and single-user Gaussian codebook design. It introduces a decoding-order constraint mechanism to drastically reduce SIC combinatorial complexity and eliminates redundant auxiliary variables and joint typicality analysis. Contribution/Results: The framework unifies single-user capacity results for multi-user interference channels for the first time. The derived achievable rate region is finite and complete under power constraints, guaranteeing lossless reconstruction of target user signals at all receivers. This establishes a low-overhead, structure-aware interference-exploitation paradigm for cell-free networks.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

We investigate the capacity region of multi-user interference channels (IC), where each user encodes multiple sub-user components. By unifying chain-rule decomposition with the Entropy Power Inequality (EPI), we reason that single-user Gaussian codebooks suffice to achieve optimal performance, thus obviating any need for intricate auxiliary variables or joint typicality arguments. Our partial-MAC formulation enumerates sub-user decoding orders while only imposing constraints for sub-users actually decoded. This significantly reduces complexity relative to enumerating all subsets or bruteforcing over all successive interference cancellation (SIC) decoding order combinations at all receivers. This leads to a finite but comprehensive construction of all achievable rate tuples under sum-power constraints, while guaranteeing that each receiver fully recovers its intended sub-user signals. Consequently, known single-user Gaussian capacity results generalize naturally to multi-user scenarios, revealing a cohesive framework for analyzing multi-user IC. Our results thus offer a streamlined, tractable pathway for designing next-generation cell-free wireless networks that rely on IC mechanisms, efficiently exploiting interference structure while minimizing overhead. Overall, this provides a unifying perspective.

Problem

• Multi-user Wireless Networks
• Interference Management
• Channel Capacity

Innovation

• Simplified Optimization
• Interference Management
• Efficient Resource Allocation