This study considers a UAV-assisted multi-user massive multiple-input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the form of an unmanned aerial vehicle (UAV) facilitates the transmission of multiple data streams from a base station (BS) to multiple Internet-of-Things (IoT) users. A joint optimization problem of hybrid beamforming (HBF), UAV relay positioning, and power allocation (PA) to multiple IoT users to maximize the total achievable rate (AR) is investigated. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and proposes three different swarm intelligence (SI)-based algorithmic solutions to optimize: 1) UAV location with equal PA; 2) PA with fixed UAV location; and 3) joint PA with UAV deployment. The radio frequency (RF) stages are designed to reduce the number of RF chains based on the slow time-varying angular information, while the baseband (BB) stages are designed using the reduced-dimension effective channel matrices. Then, a novel deep learning (DL)-based low-complexity joint hybrid beamforming, UAV location and power allocation optimization scheme (J-HBF-DLLPA) is proposed via fully-connected deep neural network (DNN), consisting of an offline training phase, and an online prediction of UAV location and optimal power values for maximizing the AR. The illustrative results show that the proposed algorithmic solutions can attain higher capacity and reduce average delay for delay-constrained transmissions in a UAV-assisted MU-mMIMO IoT systems. Additionally, the proposed J-HBF-DLLPA can closely approach the optimal capacity while significantly reducing the runtime by 99%, which makes the DL-based solution a promising implementation for real-time online applications in UAV-assisted MU-mMIMO IoT systems.

翻译：本研究考虑一种无人机辅助的多用户大规模多输入多输出（MU-mMIMO）系统，其中采用解码转发（DF）中继形式的无人机（UAV）协助基站（BS）向多个物联网（IoT）用户传输多路数据流。针对联合优化混合波束赋形（HBF）、无人机中继定位以及面向多IoT用户的功率分配（PA）以最大化总可达速率（AR）的问题进行了研究。研究采用基于几何的毫米波（mmWave）信道模型描述两类链路，并提出三种基于群体智能（SI）的算法方案分别优化：1）等功率分配下的无人机位置；2）固定无人机位置下的功率分配；3）联合无人机部署与功率分配。射频（RF）级基于慢时变角度信息设计以减少RF链路数量，基带（BB）级则利用降维有效信道矩阵进行设计。随后，通过全连接深度神经网络（DNN）提出一种新型深度学习（DL）低复杂度联合混合波束赋形、无人机定位与功率分配优化方案（J-HBF-DLLPA），其包含离线训练阶段与在线预测阶段，可获取最大化AR所需的无人机位置与最优功率值。仿真结果表明，所提算法方案能在无人机辅助MU-mMIMO IoT系统中提升时延受限传输的容量并降低平均时延。此外，所提J-HBF-DLLPA方案能紧密逼近最优容量，同时将运行时间降低99%，这使得基于深度学习的解决方案成为无人机辅助MU-mMIMO IoT系统中实时在线应用的可行方案。