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Deep Learning Meets Swarm Intelligence for UAV-Assisted IoT Coverage in Massive MIMO by Mahmood, Mobeen; Ghadaksaz, MohammadMahdi; Koc, Asil; Le-Ngoc, Tho is a scholarly article available to read on EtoBox.

What is Deep Learning Meets Swarm Intelligence for UAV-Assisted IoT Coverage in Massive MIMO about?

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

Author

Mahmood, Mobeen; Ghadaksaz, MohammadMahdi; Koc, Asil; Le-Ngoc, Tho

Published

2023

Language

EN