Energy Efficiency Optimization of RIS-Assisted UAV Search-Based Cognitive Communication in Complex Obstacle Avoidance Environments

This study investigates the use of an ant colony optimization (ACO)-based global obstacle avoidance path planning strategy in reconfigurable intelligent surface (RIS)-assisted UAV search-based communication to balance achievable rates and energy in complex urban environments. The UAV operates at low altitudes in urban areas to locate secondary users (SUs) in the secondary network and perform communication tasks, while also avoiding tall obstacles and ensuring that interference with a primary user in the primary network remains below an acceptable level. We collectively optimize the UAV flight trajectory, communication power, user scheduling, and RIS passive beamforming to achieve optimal communication quality while minimizing power consumption. This optimization problem is non-convex and involves nonlinear fractional programming. This paper proposes an alternating iterative algorithm that uses the semidefinite relaxation method for phase recovery, successive convex approximation (SCA) to address non-convex constraints, and a binary variable iterative method for the discontinuous variable issue. The simulation results show that the use of RIS can boost system performance by 101.06%. Furthermore, when comparing the overall energy efficiency of this algorithm with several benchmark methods, it was found that this algorithm outperforms others, with a maximum improvement of 165.05%. The technique proposed in this research offers significant advantages in enhancing communication capabilities in complex obstacle avoidance environments, which is crucial for advancing search-based communication for UAVs.