Multiple UAV-Assisted Cooperative DF Relaying in Multi-User Massive MIMO IoT Systems

TL;DR

The paper tackles a MU-mMIMO IoT downlink where multiple UAVs serve as decode-and-forward relays between a base station and many IoT devices. It introduces a pipeline of K-means UAV-user association, RF/BB beamforming designed from slow angular information, and a PSO-based joint UAV deployment and PA strategy to maximize the total rate $R_T$, while leveraging geometry-based mmWave channels and reduced-dimension effective channels. The key contributions are the joint UAV placement, PA, and HBF design, the two-phase SP/RP relaying model, and the demonstrated gains of multi-UAV configurations over single-UAV and fixed positioning scenarios, especially when the direct BS-to-IoT link is exploited. The work has practical implications for scalable, high-throughput UAV-assisted IoT networks in mmWave bands, where adaptive UAV positioning and power allocation can substantially boost network capacity and coverage.

Abstract

This work considers a multi-user massive multiple-input multiple-output (MU-mMIMO) Internet-of-Things (IoT) system, where multiple unmanned aerial vehicles (UAVs) operating as decode-and-forward (DF) relays connect the base station (BS) to a large number of IoT devices. To maximize the total achievable rate, we propose a novel joint optimization problem of hybrid beamforming (HBF), multiple UAV relay positioning, and power allocation (PA) to multiple IoT users. The study adopts a geometry-based millimeter-wave (mmWave) channel model for both links and utilizes sequential optimization based on K-means UAV-user association. The radio frequency (RF) stages are designed based on the slow time-varying angular information, while the baseband (BB) stages are designed utilizing the reduced-dimension effective channel matrices. The illustrative results show that multiple UAV-assisted cooperative relaying systems outperform a single UAV system in practical user distributions. Moreover, compared to fixed positions and equal PA of UAVs and BS, the joint optimization of UAV location and PA substantially enhances the total achievable rate.

Figures (5)

• Figure 1: Multiple UAV-assisted MU-mMIMO IoT communications. (a) network model. (b) UAV as DF relay transmission phases.
• Figure 2: Multiple UAV-assisted MU-mMIMO HBF system model.
• Figure 3: Achievable rate $\mathrm{R}_2$ vs. $(x-y)$-coordinates at $P_T^{(m)}$ = 20 dBm. (a) Single UAV deployment ($M=1$). (b) Multiple UAV deployment ($M=2$).
• Figure 4: Total AR $\mathrm{R}_T$ vs. $P_T$ for single and multiple UAV system.
• Figure 5: Total AR $\mathrm{R}_T$ vs. $P_T$ for 2 and 3 UAV system.