Millimeter-wave (mmWave) networks, integral to 5G communication, offer a vast spectrum that addresses the issue of spectrum scarcity and enhances peak rate and capacity. However, their dense deployment, necessary to counteract propagation losses, leads to high power consumption. An effective strategy to reduce this energy consumption in mobile networks is the sleep mode optimization (SMO) of base stations (BSs). In this paper, we propose a novel SMO approach for mmWave BSs in a 3D urban environment. This approach, which incorporates a neural network (NN) based contextual multi-armed bandit (C-MAB) with an epsilon decay algorithm, accommodates the dynamic and diverse traffic of user equipment (UE) by clustering the UEs in their respective tracking areas (TAs). Our strategy includes beamforming, which helps reduce energy consumption from the UE side, while SMO minimizes energy use from the BS perspective. We extended our investigation to include Random, Epsilon Greedy, Upper Confidence Bound (UCB), and Load Based sleep mode (SM) strategies. We compared the performance of our proposed C-MAB based SM algorithm with those of All On and other alternative approaches. Simulation results show that our proposed method outperforms all other SM strategies in terms of the $10^{th}$ percentile of user rate and average throughput while demonstrating comparable average throughput to the All On approach. Importantly, it outperforms all approaches in terms of energy efficiency (EE).

翻译：毫米波（mmWave）网络作为5G通信的核心组成部分，提供了广阔频谱以解决频谱稀缺问题，并提升了峰值速率与网络容量。然而，为克服传播损耗所需的高密度部署导致功耗显著增加。移动网络中降低能耗的有效策略之一是基站（BS）的睡眠模式优化（SMO）。本文针对三维城市环境中的毫米波基站，提出一种新型SMO方法。该方法融合了基于神经网络（NN）的上下文多臂赌博机（C-MAB）与epsilon衰减算法，通过将用户设备（UE）聚类至对应跟踪区（TA），适应其动态多样的流量特征。本策略包含波束赋形技术，可在UE侧降低能耗，而SMO则从BS角度最小化能量使用。我们扩展了研究范围，纳入随机、Epsilon贪婪、上置信界（UCB）及基于负载的睡眠模式（SM）策略。将所提出的基于C-MAB的SM算法与全开启（All On）及其他替代方案进行性能对比。仿真结果表明，所提方法在用户速率第10百分位数和平均吞吐量方面优于所有其他SM策略，且平均吞吐量与全开启方案相当。更重要的是，该方法在能效（EE）方面全面超越所有对比方案。