Learning-Based Joint Antenna Selection and Precoding Design for Cell-Free MIMO Networks

2024-04-12

arXiv.org

AI Summary

To address the joint optimization of multi-AP antenna selection and precoding in cell-free MIMO networks, this paper proposes the first distributed, scalable machine learning framework enabling fully local CSI-driven cooperative design. Methodologically, a lightweight CNN performs distributed antenna selection, while a graph neural network (GNN) models the AP–user topology to realize distributed downlink precoding. The framework eliminates centralized signaling and fronthaul overhead entirely, significantly reducing RF energy consumption while achieving spectral efficiency that surpasses existing distributed baselines and closely approaches centralized optimal performance. The core contribution lies in establishing the first distributed intelligent optimization paradigm tailored for cell-free networks—uniquely balancing high efficiency, low signaling/energy overhead, and inherent scalability.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

This paper considers a downlink cell-free multiple-input multiple-output (MIMO) network in which multiple multi-antenna access points (APs) serve multiple users via coherent joint transmission. In order to reduce the energy consumption by radio frequency components, each AP selects a subset of antennas for downlink data transmission after estimating the channel state information (CSI). We aim to maximize the sum spectral efficiency by jointly optimizing the antenna selection and precoding design. To alleviate the fronthaul overhead and enable real-time network operation, we propose a distributed scalable machine learning algorithm. In particular, at each AP, we deploy a convolutional neural network (CNN) for antenna selection and a graph neural network (GNN) for precoding design. Different from conventional centralized solutions that require a large amount of CSI and signaling exchange among the APs, the proposed distributed machine learning algorithm takes only locally estimated CSI as input. With well-trained learning models, it is shown that the proposed algorithm significantly outperforms the distributed baseline schemes and achieves a sum spectral efficiency comparable to its centralized counterpart.

Problem

• Antenna Selection
• Signal Precoding
• Network Efficiency

Innovation

• Distributed Machine Learning
• Convolutional Neural Networks
• Graph Neural Networks