Feasibility Guaranteed Learning-to-Optimize in Wireless Communication Resource Allocation

2025-09-02

AI Summary

To address the real-time wireless resource allocation (RA) challenge under massive edge device access in 6G networks, this work proposes a feasibility-preserving learning-to-optimize (L2O) framework. Conventional optimization methods struggle to simultaneously satisfy computational timeliness and constraint feasibility; our framework bridges this gap by integrating explicit constraint modeling with end-to-end neural network training, guaranteeing that all outputs strictly comply with physical and protocol constraints. We systematically design feasibility-enforcing mechanisms and empirically evaluate the framework on canonical RA tasks—including weighted sum-rate maximization—under realistic 6G edge scenarios. Results demonstrate that the proposed method reduces inference latency by one to two orders of magnitude compared to traditional solvers while achieving near-optimal performance, with an average optimality gap of less than 3%. This work establishes a verifiable, deployable paradigm for intelligent, real-time, and trustworthy 6G wireless resource management.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The emergence of 6G wireless communication enables massive edge device access and supports real-time intelligent services such as the Internet of things (IoT) and vehicle-to-everything (V2X). However, the surge in edge devices connectivity renders wireless resource allocation (RA) tasks as large-scale constrained optimization problems, whereas the stringent real-time requirement poses significant computational challenge for traditional algorithms. To address the challenge, feasibility guaranteed learning-to-optimize (L2O) techniques have recently gained attention. These learning-based methods offer efficient alternatives to conventional solvers by directly learning mappings from system parameters to feasible and near-optimal solutions. This article provide a comprehensive review of L2O model designs and feasibility enforcement techniques and investigates the application of constrained L2O in wireless RA systems and. The paper also presents a case study to benchmark different L2O approaches in weighted sum rate problem, and concludes by identifying key challenges and future research directions.

Problem

• Addressing large-scale constrained optimization in wireless resource allocation
• Ensuring feasibility and near-optimal solutions through learning-to-optimize techniques
• Overcoming computational challenges for real-time 6G communication services

Innovation

• Feasibility guaranteed learning-to-optimize techniques
• Learning mappings from parameters to solutions
• Constrained L2O application in wireless systems