Enhancing Regional Signal-to-Noise Ratio for Flying UAV via Intelligent Reflecting Surface

In future 6G communications, unmanned aerial vehicle (UAV) will play a vital role in urban area communication system. However, the link between low-altitude UAV and base station (BS) will inevitably be blocked by buildings, resulting in severe performance degradation. Intelligent reflecting surface (IRS) is a cost-effective technique that can improve the link quality by intelligently reflecting signals. Motivated by this, an IRS-assisted UAV communication system is studied in this paper to enhance the communication quality between BS and UAV within UAV’s flying area. Specifically, the regional signal-to-noise ratio (R-SNR) is maximized for the blocked area. Due to the non-convexity of the problem, we first decouple the parameters in the formulated problem, and then propose a gradient descent algorithm with suspensive condition (GDSC) to obtain an asymptotic-optimal solution. The convergence property of the proposed GDSC is proved in this paper. Furthermore, a particle swarm optimization-aided alternating (PSOA) algorithm is then proposed, in which we screen the starting points to reduce the complexity. The simulation results reveal the two proposed algorithms achieve near-optimal performance and approach the optimal solution provided by the exhaustive search scheme, and outperform the traditional gradient descent algorithm by up to 28.5%.