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

TL;DR

OpenRANet introduces a deep learning framework that efficiently solves joint subcarrier and power allocation in Open RAN, reducing power consumption while satisfying user data rate demands through an innovative integration of optimization and machine learning.

Contribution

The paper presents a novel optimization-based deep learning model that transforms a nonconvex problem into convex subproblems, enabling efficient resource allocation in Open RAN networks.

Findings

Enhanced power efficiency in resource allocation.

Improved constraint satisfaction and solution accuracy.

Scalable approach applicable to complex wireless scenarios.

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.