发表机构
University of Salento; University of Liverpool（萨伦托大学; 利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对5G网络基站能耗高、DRL训练负担重的问题，提出基于参数化量子电路的量子强化学习算法，利用叠加和纠缠原理加速收敛，在动态UE场景下降低能耗并保持QoS，优于DRL和Q-Learning。
AI 中文摘要
节能已成为5G及未来网络中的一个关键挑战。连接设备的快速增长增加了整体网络能源需求，将运营支出推至不可持续的水平。基站（BS）占能源使用量的最大份额，通常消耗无线接入网（RAN）总能源的约60-70%。因此，为解决这一问题，本文在考虑用户设备（UE）动态行为的同时，优化基站的能源使用。深度强化学习（DRL）是确定有效节能策略的自然候选方案，例如在用户密度低时自动开启或关闭基站，或调整发射功率以平衡能源效率和服务质量（QoS）。然而，其沉重的训练负担以及密集5G环境中状态和动作空间的指数增长使得探索变得越来越困难。为克服这些限制，我们引入了一种新颖的量子强化学习（QRL）算法，该算法通过参数化量子电路利用量子原理，包括叠加和纠缠，从而比依赖传统深度神经网络的DRL实现显著更快的收敛。大量模拟表明，即使UE高度动态且频繁切换其与基站天线的关联，所提出的QRL也能在保持QoS的同时大幅降低能源消耗。此外，QRL在收敛速度和学习复杂度方面始终优于DRL和Q-Learning。
英文摘要
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.
