RIS-Assisted UAV-Enabled Green Communications for Industrial IoT Exploiting Deep Learning

Industrial Internet of Things (IIoT), regarded as an important technology for Industry 4.0, has the capability to connect massive IoT devices anywhere and at anytime in manufacturing industry. Enabling such a huge network requires message delivering among sensors, actuators, controllers, and the remote control to be seamless and reliable. However, IIoT wireless environment typically faces challenges such as blockage caused by IoT obstacles. To tackle the above issue, the unmanned aerial vehicle (UAV) and the reconfigurable intelligent surface (RIS) are exploited in this paper, which can provide favorable air-to-ground links and further rebuild the wireless channels. Moreover, the device-to-device (D2D) communication technique is introduced to enable direct information exchange between IoT devices. Specifically, we consider both the communication between the UAV and the cellular users (e.g., fixed IoT infrastructures) as well as the communication between D2D users (e.g., mobile IoT devices). Instead of only considering throughput, we focus on energy efficiency optimization for D2D users while guaranteeing the quality of service for cellular users, since energy-efficient transmission or green communication is important for IIoT scenarios. The transmit power, channel allocation parameters, and RIS's reflection coefficients are jointly optimized to maximize energy efficiency for D2D users. To solve the formulated optimization problem, both centralized and distributed optimization algorithms based on deep neural networks are provided. Simulation results show that the introduction of RIS can significantly improve system performance. Moreover, the proposed algorithms can approximate the optimal solutions without the need of exhaustive search.