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).

本研究提出了一种用于毫米波基站的睡眠模式优化方法，在 3D 城市环境中通过神经网络和上下文多臂老虎机算法对用户设备进行动态和多样化的流量聚类，采用波束成形技术降低用户设备的能耗，并通过睡眠模式优化策略从基站的角度减少能量消耗，仿真结果表明该方法在用户速率和平均吞吐量上优于其他睡眠模式策略，并在能量效率方面表现出色。

基于深度上下文多臂赌博算法的 5G 毫米波网络高能效休眠模式优化

Energy-Efficient Sleep Mode Optimization of 5G mmWave Networks Using  Deep Contextual MAB

In this paper, we develop a deep learning (DL)-guided hybrid beam and power
allocation approach for multiuser millimeter-wave (mmWave) networks, which
facilitates swift beamforming at the base station (BS). The following
persisting challenges motivated our research: (i) User and vehicular mobility,
as well as redundant beam-reselections in mmWave networks, degrade the
efficiency; (ii) Due to the large beamforming dimension at the BS, the
beamforming weights predicted by the cutting-edge DL-based methods often do not
suit the channel distributions; (iii) Co-located user devices may cause a
severe beam conflict, thus deteriorating system performance. To address the
aforementioned challenges, we exploit the synergy of supervised learning and
super-resolution technology to enable low-overhead beam- and power allocation.
In the first step, we propose a method for beam-quality prediction. It is based
on deep learning and explores the relationship between high- and low-resolution
beam images (energy). Afterward, we develop a DL-based allocation approach,
which enables high-accuracy beam and power allocation with only a portion of
the available time-sequential low-resolution images. Theoretical and numerical
results verify the effectiveness of our proposed

本文提出了基于深度学习和超分辨技术的低开销光束和功率分配方法，以应对多用户毫米波网络中用户和车辆的移动、不必要的光束重新选择和定位等难题，并通过理论和数值分析结果证明了该方法的有效性。

深度学习和图像超分辨率引导的毫米波网络束及功率分配

Deep Learning and Image Super-Resolution-Guided Beam and Power  Allocation for mmWave Networks

This study demonstrates the feasibility of the proactive received power
prediction by leveraging spatiotemporal visual sensing information toward the
reliable millimeter-wave (mmWave) networks. Since the received power on a
mmWave link can attenuate aperiodically due to a human blockage, the long-term
series of the future received power cannot be predicted by analyzing the
received signals before the blockage occurs. We propose a novel mechanism that
predicts a time series of the received power from the next moment to even
several hundred milliseconds ahead. The key idea is to leverage the camera
imagery and machine learning (ML). The time-sequential images can involve the
spatial geometry and the mobility of obstacles representing the mmWave signal
propagation. ML is used to build the prediction model from the dataset of
sequential images labeled with the received power in several hundred
milliseconds ahead of when each image is obtained. The simulation and
experimental evaluations using IEEE 802.11ad devices and a depth camera show
that the proposed mechanism employing convolutional LSTM predicted a time
series of the received power in up to 500 ms ahead at an inference time of less
than 3 ms with a root-mean-square error of 3.5 dB.

本研究通过利用时空视觉传感信息，展示了主动接收功率预测在可靠毫米波网络中的可行性。通过使用摄像头图像和机器学习，该机制可以预测从下一时刻到几百毫秒后的接收功率时间序列。通过卷积 LSTM 的使用，该机制可以在不到 3ms 的推理时间内，以 3.5 dB 的均方根误差预测接收功率的时间序列高达 500ms。