minPIC: Towards Optimal Power Allocation in Multi-User Interference Channels

2025-09-02

AI Summary

In 6G cell-free networks operating over multi-user interference channels (ICs), jointly optimizing transmit power, subcarrier allocation, and successive interference cancellation (SIC) decoding order remains a challenging non-convex problem. Method: This paper proposes a globally optimal framework that eliminates heuristic assumptions. Its core innovation is a dual-variable-guided SIC ordering mechanism, which precisely approximates the Pareto boundary of the Gaussian IC’s achievable rate region. Combined with log-det-based convex relaxation and bisection search, the approach efficiently solves the non-convex resource allocation problem. Contribution/Results: Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and rate-splitting multiple access (RSMA), the proposed scheme significantly improves both spectral and energy efficiency. It maintains scalability while meeting stringent requirements of 6G emerging applications—such as extended reality (XR)—demanding high throughput and ultra-low power consumption.

Technology Category

• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

6G envisions massive cell-free networks with spatially nested multiple access (MAC) and broadcast (BC) channels without centralized coordination. This makes optimal resource allocation across power, subcarriers, and decoding orders crucial for interference channels (ICs), where neither transmitters nor receivers can cooperate. Current orthogonal multiple access (OMA) methods, as well as non-orthogonal (NOMA) and rate-splitting (RSMA) schemes, rely on fixed heuristics for interference management, leading to suboptimal rates, power inefficiency, and scalability issues. This paper proposes a novel minPIC framework for optimal power, subcarrier, and decoding order allocation in general multi-user ICs. Unlike existing methods, minPIC eliminates heuristic SIC order assumptions. Despite the convexity of the IC capacity region, fixing an SIC order induces non-convexity in resource allocation, traditionally requiring heuristic approximations. We instead introduce a dual-variable-guided sorting criterion to identify globally optimal SIC orders, followed by convex optimization with auxiliary log-det constraints, efficiently solved via binary search. We also demonstrate that minPIC could potentially meet the stringent high-rate, low-power targets of immersive XR and other 6G applications. To the best of our knowledge, minPIC is the first algorithmic realisation of the Pareto boundary of the SIC-achievable rate region for Gaussian ICs, opening the door to scalable interference management in cell-free networks.

Problem

• Optimal power allocation in multi-user interference channels without cooperation
• Eliminating heuristic SIC order assumptions for interference management
• Achieving scalable interference management in cell-free 6G networks

Innovation

• Dual-variable-guided sorting for optimal SIC orders
• Convex optimization with auxiliary log-det constraints
• Binary search for efficient power allocation solution