用强化学习优化光无线混合网络用户接入，提升吞吐量与公平性。
Deep Reinforcement Learning-Based User Association in Hybrid LiFi/WiFi Indoor Networks
- 基于深度强化学习设计用户接入算法，解决多接入点选择难题。
- 相比信号强度策略，吞吐量提升32.25%，公平性提升19.09%。
- 适用于高密度室内场景的智能资源分配，适合网络优化研究者。
混合光保真（LiFi）与无线保真（WiFi）室内网络被视为缓解射频频谱紧张、满足室内日益增长的数据速率需求的有前景技术。该网络可结合LiFi高速传输与WiFi广覆盖优势，显著优于独立网络。但其共存面临用户接入、移动性支持及高效资源分配等挑战。本文旨在设计一种新用户-接入点（AP）关联算法，以最大化网络总吞吐量。首先，考虑用户移动性和实际容量限制，建立总数据速率最大化问题，其为非凸二值整数规划问题。随后提出基于序列近端策略优化（S-PPO）的深度强化学习方法求解。通过与穷举搜索（ES）、信号强度策略（SSS）及信任域策略优化（TRPO）对比进行大量仿真。结果表明，所提算法平均比SSS提升32.25%吞吐量和19.09%公平性，比TRPO提升10.34%吞吐量和10.23%公平性。
原文摘要 · Abstract (English)
Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) indoor networks has been envisioned as a promising technology to alleviate radio frequency spectrum crunch to accommodate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can leverage the advantages of fast data transmission from LiFi and wider coverage of WiFi, thus complementing well with each other and further improving the network performance compared with the standalone networks. However, to leverage the co-existence, several challenges should be addressed, including but not limited to user association, mobility support, and efficient resource allocation. Therefore, the objective of the paper is to design a new user-access point association algorithm to maximize the sum throughput of the hybrid networks. We first mathematically formulate the sum data rate maximization problem by determining the AP selection for each user in indoor networks with consideration of user mobility and practical capacity limitations, which is a nonconvex binary integer programming problem. To solve this problem, we then propose a sequential-proximal policy optimization (S-PPO) based deep reinforcement learning method. Extensive simulations are conducted to evaluate the proposed method by comparing it with exhaustive search (ES), signal strength strategy (SSS), and trust region policy optimization (TRPO) methods. Comprehensive simulation results demonstrate that our solution algorithm can outperform SSS by about 32.25% of the sum throughput and 19.09% of the fairness on average, and outperform TRPO by about 10.34% and 10.23%, respectively.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。