基于深度上下文多臂老虎机的5G毫米波网络高能效睡眠模式优化
Energy-Efficient Sleep Mode Optimization of 5G mmWave Networks Using Deep Contextual MAB
AI总结：
针对5G毫米波网络密集部署导致的高功耗问题，提出结合神经网络、epsilon衰减算法与UE聚类的上下文多臂老虎机睡眠模式优化方法，兼顾波束赋形降能耗，在用户速率、吞吐量与能效上表现优异。
摘要：
毫米波（mmWave）网络是5G通信的核心组成部分，拥有丰富的频谱资源，可解决频谱稀缺问题并提升峰值速率和容量。然而，为抵消传播损耗而进行的密集部署导致了高功耗。降低移动网络能耗的一种有效策略是基站（BS）的睡眠模式优化（SMO）。本文提出了一种适用于三维城市环境中毫米波基站的新型SMO方法。该方法将基于神经网络（NN）的上下文多臂老虎机（C-MAB）与epsilon衰减算法相结合，通过在各自的跟踪区域（TA）内对用户设备（UE）进行聚类，适配UE动态且多样的业务量。我们的策略包含波束赋形技术，可从UE侧降低能耗，而SMO则从基站角度最大限度减少能源使用。我们将研究扩展至Random（随机）、Epsilon Greedy（ε-贪心）、Upper Confidence Bound（UCB，上置信界）和Load Based（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）策略及其他替代方法的性能进行了对比。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All On方法相当。重要的是，该方法在能效（EE）方面优于所有对比方法。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
