Drone-Mounted Intelligent Reflecting Surface-Assisted Multiple-Input Multiple-Output Communications for 5G-and-Beyond Internet of Things Networks: Joint Beamforming, Phase Shift Design, and Deployment Optimization

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes.