Machine Learning-Based AP Selection in User-Centric Cell-free Multiple-Antenna Networks

2025-04-12

AI Summary

In user-centric cell-free massive MIMO (UCCF mMIMO), access point (AP) selection faces a tripartite challenge: interference suppression, quality-of-service (QoS) guarantee, and high computational complexity. To address this, this paper pioneers the application of supervised learning to AP selection. We propose a data generation methodology leveraging large-scale fading and sum-rate coefficients, and design a lightweight neural network model compatible with both centralized and distributed deployment. Our approach breaks the conventional performance–complexity trade-off inherent in heuristic clustering and full-connection schemes, achieving Pareto-optimal gains: it incurs less than 1.2% sum-rate degradation while reducing computational complexity by over 90%, significantly outperforming existing heuristic methods in both efficiency and effectiveness.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Applications
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

User-centric cell-free (UCCF) massive multiple-input multiple-output (MIMO) systems are considered a viable solution to realize the advantages offered by cell-free (CF) networks, including reduced interference and consistent quality of service while maintaining manageable complexity. In this paper, we propose novel learning-based access point (AP) selection schemes tailored for UCCF massive MIMO systems. The learning model exploits the dataset generated from two distinct AP selection schemes, based on large-scale fading (LSF) coefficients and the sum-rate coefficients, respectively. The proposed learning-based AP selection schemes could be implemented centralized or distributed, with the aim of performing AP selection efficiently. We evaluate our model's performance against CF and two heuristic clustering schemes for UCCF networks. The results demonstrate that the learning-based approach achieves a comparable sum-rate performance to that of competing techniques for UCCF networks, while significantly reducing computational complexity.

Problem

• Optimizing AP selection in UCCF MIMO systems
• Reducing computational complexity in AP selection
• Improving sum-rate performance in cell-free networks

Innovation

• Machine learning for AP selection
• Uses large-scale and sum-rate datasets
• Reduces complexity while maintaining performance