Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

By Yifei Shen, Yuanming Shi, Jun Zhang, Khaled Ben Letaief. IEEE Journal on Selected Areas in Communications, 2020

393 citations recorded by the source corpus.

Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis. Specifically, we first demonstrate that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property. We then identify a family of neural networks, named message passing graph neural networks (MPGNNs). It is demonstrated that they not only satisfy the permutation equivariance property, but also can generalize to large-scale problems, while enjoying a high computational efficiency. For interpretablity and theoretical guarantees, we prove the equivalence between MPGNNs and a family of distributed optimization algorithms, which is then used to analyze the performance and generalization of MPGNN-based methods. Extensive simulations, with power control and beamforming as two examples, demonstrate that the proposed method, trained in an unsupervised manner with unlabeled samples, matches or even outperforms classic optimization-based algorithms without domain-specific knowledge. Remarkably, the proposed method is highly scalable and can solve the beamforming problem in an interference channel with 1000 transceiver pairs within 6 milliseconds on a single GPU.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/661a0ed58f6b2ef07714e93f980bd9ae55d00492

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Citations in the corpus

Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach (2021, neutral): “…data-oriented communication , including transceiver structures [7], source/channel coding [8], signal detection [9], and radio resource management [10], have been revisited intensively using AI techniques, especially deep neural networks (DNNs), breeding the emerging area of “ learning to…”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, positive): “For example, a graph neural network (GNN) was applied to assist radio resource management in [213] and its complexiy is O(LD), where L is the number of layers of the GNN and D is the maximal degrees of the graph.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “optimize nonconvex problems, including power control [270], beamforming [23], and phase shift design [25].”