Graph-Based Algorithm Unfolding for Energy-Aware Power Allocation in Wireless Networks

• Published in: IEEE transactions on wireless communications Vol. 22; no. 2; pp. 1359 - 1373
• Main Authors: Li, Boning, Verma, Gunjan, Segarra, Santiago
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Communication networks; Computer architecture; deep algorithm unfolding; Energy efficiency; Energy management; graph convolutional neural networks; Interference; Iterative methods; Maximum power; Modular structures; multi-user multi-cell interference; Network topologies; Neural networks; Permutations; Resource management; Robustness (mathematics); weighted sum energy efficiency maximization; Wireless communication; Wireless communications; Wireless networks; Wireless power allocation; Wireless sensor networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3204486

We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists of modular structures inspired by a classical iterative suboptimal approach and enhanced with learnable components. More precisely, we propose a deep unfolding of the successive concave approximation (SCA) method. In our unfolded SCA (USCA) framework, the originally preset parameters are now learnable via graph convolutional neural networks (GCNs) that directly exploit multi-user channel state information as the underlying graph adjacency matrix. We show the permutation equivariance of the proposed architecture, which is a desirable property for models applied to wireless network data. The USCA framework is trained through a stochastic gradient descent approach using a progressive training strategy. The unsupervised loss is carefully devised to feature the monotonic property of the objective under maximum power constraints. Comprehensive numerical results demonstrate its generalizability across different network topologies of varying size, density, and channel distribution. Thorough comparisons illustrate the improved performance and robustness of USCA over state-of-the-art benchmarks.