Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning

• Published in: IEEE transactions on wireless communications Vol. 22; no. 12; p. 1
• Main Authors: Peng, Bile, Besser, Karl-Ludwig, Raghunath, Ramprasad, Jorswieck, Eduard A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Complexity; Convexity; Design optimization; Energy efficiency; Interference; Machine learning; Machine learning algorithms; Neural networks; non-convex optimization; Optimization; permutation-equivariance; Permutations; Power control; reparameterization trick; Tensors; Unsupervised learning; Wireless networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2023.3269770

This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity are proposed. In contrast, we propose an unsupervised machine learning framework to approach the global optimum. While the neural network (NN) training takes moderate time, application with the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture SINRnet for the power allocation problems in the interference channel that is permutation-equivariant. We encode our domain knowledge into the NN design and shed light into the black box of machine learning. Training and testing results show that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm and outperform the successive convex approximation (SCA) algorithm. Hence, the proposed approach balances between computational complexity and performance.