Title: Diffusion-RL for Scalable Resource Allocation for 6G Networks Show Chinese title

Title: 用于6G网络可扩展资源分配的扩散-强化学习

Abstract: This paper presents a novel approach to resource allocation in Open Radio Access Networks (O-RAN), leveraging a Generative AI technique with network slicing to address the diverse demands of 5G and 6G service types such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and Massive Machine-Type Communications (mMTC). Additionally, we provide a comprehensive analysis and comparison of machine learning (ML) techniques for resource allocation within O-RAN, evaluating their effectiveness in optimizing network performance. We introduce a diffusion-based reinforcement learning (Diffusion-RL) algorithm designed to optimize the allocation of physical resource blocks (PRBs) and power consumption, thereby maximizing weighted throughput and minimizing the delay for user equipment (UE). The Diffusion-RL model incorporates controlled noise and perturbations to explore optimal resource distribution while meeting each service type's Quality of Service (QoS) requirements. We evaluate the performance of our proposed method against several benchmarks, including an exhaustive search algorithm, deep Q-networks (DQN), and the Semi-Supervised Variational Autoencoder (SS-VAE). Comprehensive metrics, such as throughput and latency, are presented for each service type. Experimental results demonstrate that the Diffusion-based RL approach outperforms existing methods in efficiency, scalability, and robustness, offering a promising solution for resource allocation in dynamic and heterogeneous O-RAN environments with significant implications for future 6G networks. Show Chinese abstract

Abstract: 本文提出了一种针对开放无线接入网络（O-RAN）资源分配的新方法，利用生成式人工智能技术与网络切片相结合，以应对5G和6G服务类型（如增强移动宽带（eMBB）、超高可靠低时延通信（URLLC）以及大规模机器类型通信（mMTC））的多样化需求。 此外，我们还提供了对O-RAN内资源分配的机器学习（ML）技术的全面分析与比较，评估了它们优化网络性能的有效性。 我们引入了一种基于扩散的强化学习（Diffusion-RL）算法，旨在优化物理资源块（PRBs）和功率消耗的分配，从而最大化加权吞吐量并最小化用户设备（UE）的延迟。 Diffusion-RL 模型通过引入受控噪声和扰动来探索最优的资源分布，同时满足每种服务类型的 QoS（服务质量）要求。 我们将所提出的方法与包括穷举搜索算法、深度 Q 网络（DQN）和半监督变分自编码器（SS-VAE）在内的多个基准进行了对比评估。 为每种服务类型提供了包括吞吐量和延迟在内的综合指标。 实验结果表明，基于扩散的 RL 方法在效率、可扩展性和鲁棒性方面优于现有方法，为动态异构 O-RAN 环境中的资源分配提供了有前景的解决方案，并对未来 6G 网络具有重要意义。