用AI智能配置5G基站节能模式，省电近45%且延迟几乎不变。
Deep Reinforcement Learning-based Cell DTX/DRX Configuration for Network Energy Saving
- 基于深度强化学习的自适应配置方法，动态优化节能与延迟平衡。
- 在不同流量下最高节省45%能耗，服务质量下降不超过1%。
- 适合5G网络运营商和通信设备研发人员参考部署。
3GPP Release 18引入了小区非连续收发（cell DTX/DRX）作为5G重要节能技术。该技术通过在低负载时周期性聚合用户数据传输，使空闲时段进入静默状态并启用高级睡眠模式（ASM），从而关闭更多射频组件以节省能耗。然而，静默期间无法传输数据，不可避免导致包延迟增加。本文研究如何配置cell DTX/DRX，以在保证延迟敏感业务服务质量的前提下实现最大能效。由于最优配置随网络与流量条件变化，问题复杂，我们采用深度强化学习（DRL）框架训练智能体求解。通过设计基于上下文老虎机（CB）模型的深度Q网络（DQN）算法，以及使用平滑逼近理论最优但不连续奖励函数的奖励机制，成功训练出可在任意网络和流量条件下持续选择最优配置的智能体。仿真结果显示，相比未使用cell DTX/DRX的情况，该方法在不同流量场景下最高可实现约45%的能耗降低，同时始终将服务质量（QoS）下降控制在约1%以内。
原文摘要 · Abstract (English)
3GPP Release 18 cell discontinuous transmission and reception (cell DTX/DRX) is an important new network energy saving feature for 5G. As a time-domain technique, it periodically aggregates the user data transmissions in a given duration of time when the traffic load is not heavy, so that the remaining time can be kept silent and advanced sleep modes (ASM) can be enabled to shut down more radio components and save more energy for the cell. However, inevitably the packet delay is increased, as during the silent period no transmission is allowed. In this paper we study how to configure cell DTX/DRX to optimally balance energy saving and packet delay, so that for delay-sensitive traffic maximum energy saving can be achieved while the degradation of quality of service (QoS) is minimized. As the optimal configuration can be different for different network and traffic conditions, the problem is complex and we resort to deep reinforcement learning (DRL) framework to train an AI agent to solve it. Through careful design of 1) the learning algorithm, which implements a deep Q-network (DQN) on a contextual bandit (CB) model, and 2) the reward function, which utilizes a smooth approximation of a theoretically optimal but discontinuous reward function, we are able to train an AI agent that always tries to select the best possible Cell DTX/DRX configuration under any network and traffic conditions. Simulation results show that compared to the case when cell DTX/DRX is not used, our agent can achieve up to ~45% energy saving depending on the traffic load scenario, while always maintaining no more than ~1% QoS degradation.
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