🤖 AI Summary
This paper addresses the non-convex joint subcarrier and power allocation problem in Open RAN, aiming to minimize base station total power consumption while strictly satisfying user rate constraints.
Method: We propose an optimization-driven, end-to-end trainable framework that embeds a differentiable convex optimization layer into a neural network. Through variable transformation, problem decomposition, and constraint relaxation, we achieve differentiable modeling of Lagrangian dual decomposition and iterative interference function algorithms.
Contribution/Results: The architecture ensures strict adherence to physical constraints, strong generalization across network topologies, and real-time inference capability. Experiments demonstrate an average 18.7% reduction in power consumption, a 200× speedup in inference time over conventional optimization methods, a constraint violation rate below 0.3%, and seamless support for complex deployments—including integrated satellite-terrestrial networks.
📝 Abstract
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.