arXiv：cs.LG· Muhammad Usman, Nguyen Van Huynh, Marianna Lezzi, Mariangela Lazoi·· 3 小时前AI 评分30
5G 及未来网络的节能：一种量子强化学习方法
Energy Saving in 5G and Beyond Networks: A Quantum Reinforcement Learning Approach
AI 导读
研究提出一种量子强化学习（QRL）算法，通过参数化量子电路利用叠加与纠缠原理，为 5G 及未来网络优化基站能耗，收敛速度显著快于依赖深度神经网络的 DRL。仿真显示，即使用户设备高度动态、频繁切换基站天线关联，QRL 仍能在维持 QoS 的同时大幅降低能耗，并在收敛速度和学习复杂度上持续优于 DRL 与 Q-Learning。基站通常占无线接入网总能耗的 60-70%。
正文
Abstract:Energy saving has become a critical challenge in 5G and beyond networks. The rapid growth of connected devices has increased the overall network energy demand, driving operational expenditure to unsustainable heights. The Base Station (BS) accounts for the largest share of energy usage, typically consuming around 60-70\% of the Radio Access Network (RAN)'s total energy. Therefore, to address this issue, this article optimizes the BS's energy usage while accounting for the dynamic behavior of User Equipment (UE). Deep Reinforcement Learning (DRL) is a natural candidate for determining effective energy saving policies, such as automatically switching BSs on or off when user density is low or adjusting transmission power to balance energy efficiency and Quality of Service (QoS). However, its heavy training burden and the exponential growth of state and action spaces in dense 5G environments make exploration increasingly difficult. To overcome these limitations, we introduce a novel Quantum Reinforcement Learning (QRL) algorithm that leverages quantum principles, including superposition and entanglement, through parameterized quantum circuits, enabling significantly faster convergence than DRL, which relies on conventional deep neural networks. Extensive simulations demonstrate that the proposed QRL can substantially reduce energy consumption while maintaining QoS, even when UEs are highly dynamic and frequently switch their association with BS antennas. Additionally, QRL consistently outperforms DRL and Q-Learning in both convergence speed and learning complexity.
| Subjects: | Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.02403 [cs.NI] |
| (or arXiv:2610.02403v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02403 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Muhammad Usman [view email]
[v1]
Thu, 1 Oct 2026 19:30:42 UTC (1,214 KB)
来源：arXiv：cs.LG · arxiv.org