A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks

Abstract

This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of the most common energy-efficient power control problems with a complexity that, although still exponential in the number of variables, is much lower than other available global optimization frameworks. Moreover, the reduced complexity of the proposed framework allows its practical implementation through the use of deep neural networks. Specifically, thanks to its reduced complexity, the proposed method can be used to train an artificial neural network to predict the optimal resource allocation. This is in contrast with other power control methods based on deep learning, which train the neural network based on suboptimal power allocations due to the large complexity that generating large training sets of optimal power allocations would have with available global optimization methods. As a benchmark, we also develop a novel first-order optimal power allocation algorithm. Numerical results show that a neural network can be trained to predict the optimal power allocation policy.

submitted

References in corpus (6)

• Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management
• Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
• Mixed Monotonic Programming for Fast Global Optimization
• Energy efficiency optimization in MIMO interference channels: A successive pseudoconvex approximation approach
• Efficient Global Optimal Resource Allocation in Non-Orthogonal Interference Networks
• Optimization of Weighted Individual Energy Efficiencies in Interference Network

Cited by in corpus (15)

• A Deep Learning Framework for Optimization of MISO Downlink Beamforming
• AI based Service Management for 6G Green Communications
• Satellite edge computing for real-time and very-high resolution Earth observation
• Mixed Max-and-Min Fractional Programming for Wireless Networks
• Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks
• Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling
• Learning Based Dynamic Cluster Reconfiguration for UAV Mobility Management with 3D Beamforming
• Hierarchical Multi-Agent DRL Based Dynamic Cluster Reconfiguration for UAV Mobility Management
• Learning to Transmit with Provable Guarantees in Wireless Federated Learning
• Reinforcement Learning Based Dynamic Power Control for UAV Mobility Management
• On Learning-Assisted Content-Based Secure Image Transmission for Delay-Aware Systems with Randomly-Distributed Eavesdroppers -- Extended Version
• Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems: A Tutorial Overview
• Artificial Intelligence for 5G Wireless Systems: Opportunities, Challenges, and Future Research Directions
• Design of RIS-aided mMTC+ Networks for Rate Maximization under the Finite Blocklength Regime with Imperfect Channel Knowledge
• Welfare Measure for Resource Allocation with Algorithmic Implementation: Beyond Average and Max-Min