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-aware 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.

翻译：6G无线通信的出现使得海量边缘设备接入成为可能，并支持物联网（IoT）和车联网（V2X）等实时智能服务。然而，边缘设备连接数量的激增使无线资源分配（RA）任务转化为大规模约束优化问题，而严格的实时性要求对传统算法构成了显著的计算挑战。为应对这一挑战，可行性感知学习优化（L2O）技术近年来受到广泛关注。这类基于学习的方法通过直接从系统参数学习到可行且接近最优解的映射，为传统求解器提供了高效替代方案。本文系统综述了L2O模型设计与可行性保障技术，并探讨了约束L2O在无线RA系统中的应用。论文还通过加权和速率问题的案例研究对不同L2O方法进行了性能比较，最后指出了关键挑战与未来研究方向。