OpenRANet: Neuralized Spectrum Access by Joint Subcarrier and Power Allocation With Optimization-based Deep Learning

作     者：Chen, Siya Tan, Chee Wei Zhai, Xiangping Poor, H. Vincent 

作者机构：City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Nanyang Technological University Singapore Singapore Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210023 China Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States 

出 版 物：《IEEE Transactions on Green Communications and Networking》 (IEEE Trans. Green Commun. Networking)

年 卷 期：2025年

核心收录：

主　　题：Convex optimization 

摘      要：The next-generation radio access network (RAN), known as Open RAN, is poised to feature an AI-native interface for wireless cellular networks, including emerging satellite-terrestrial systems, making deep learning integral to its operation. In this paper, we address the nonconvex optimization challenge of joint subcarrier and power allocation in Open RAN, with the objective of minimizing the total power consumption while ensuring users meet their transmission data rate requirements. We propose OpenRANet, an optimization-based deep-learning model that integrates machine-learning techniques with iterative optimization algorithms. We start by transforming the original nonconvex problem into convex subproblems through decoupling, variable transformation, and relaxation techniques. These subproblems are then efficiently solved using iterative methods within the standard interference function framework, enabling the derivation of primal-dual solutions. These solutions integrate seamlessly as a convex optimization layer within OpenRANet, enhancing constraint adherence, solution accuracy, and computational efficiency by combining machine learning with convex analysis, as shown in numerical experiments. OpenRANet also serves as a foundation for designing resource-constrained AI-native wireless optimization strategies for broader scenarios like multi-cell systems, satellite-terrestrial networks, and future Open RAN deployments with complex power consumption requirements. © 2017 IEEE.