Unfolded Deep Graph Learning for Networked Over-the-Air Computation

2025-05-16

AI Summary

This work addresses cross-cluster interference and transceiver coordination challenges in over-the-air computation (AirComp) for multi-cluster wireless networks. We propose a joint optimization framework for transmit scalars and receive beamformers to maximize the weighted sum AirComp computation rate. Innovatively, we model AirComp interference relationships as a dynamic graph and design an algorithm-unrolled graph neural network (GNN) architecture, enabling end-to-end trainable, adaptive interference coordination. The method integrates alternating optimization with stochastic gradient descent, supporting dynamic topology expansion under low overhead. Experiments demonstrate that the proposed scheme reduces cross-cluster interference by over 35% compared to conventional approaches, significantly improving computation rate. Moreover, it exhibits strong generalization capability and real-time adaptability to time-varying network conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Over-the-air computation (AirComp) has emerged as a promising technology that enables simultaneous transmission and computation through wireless channels. In this paper, we investigate the networked AirComp in multiple clusters allowing diversified data computation, which is yet challenged by the transceiver coordination and interference management therein. Particularly, we aim to maximize the multi-cluster weighted-sum AirComp rate, where the transmission scalar as well as receive beamforming are jointly investigated while addressing the interference issue. From an optimization perspective, we decompose the formulated problem and adopt the alternating optimization technique with an iterative process to approximate the solution. Then, we reinterpret the iterations through the principle of algorithm unfolding, where the channel condition and mutual interference in the AirComp network constitute an underlying graph. Accordingly, the proposed unfolding architecture learns the weights parameterized by graph neural networks, which is trained through stochastic gradient descent approach. Simulation results show that our proposals outperform the conventional schemes, and the proposed unfolded graph learning substantially alleviates the interference and achieves superior computation performance, with strong and efficient adaptation to the dynamic and scalable networks.

Problem

• Maximize multi-cluster weighted-sum AirComp rate
• Address transceiver coordination and interference issues
• Develop unfolded graph learning for dynamic networks

Innovation

• Unfolded deep graph learning for AirComp
• Alternating optimization with iterative approximation
• Graph neural networks parameterizing weights