Optimizing UAV Base Station Positioning through Quantum-Inspired Solution Workflow

UAV base stations play a pivotal role in providing wireless connectivity where terrestrial networks face limitations. User Equipment (UE) relies on these UAV-based solutions for enhanced data rates and coverage. This novel approach incorporates Spiking Neural Networks (SNNs) within Reinforcement Learning (RL) frameworks, a pioneering exploration in this field. The principal aims are to optimize data rates for User Equipment (UE) while minimizing latency, expanding coverage, and improving network performance, all within the limitations of the restricted number of Unmanned Aerial Vehicle (UAV) base stations. Employing a quantum-inspired clustering technique, Q-Means, we optimize UE allocation to UAV base stations, significantly reducing computational complexity. SNNs, capable of event-based information processing, offer more efficient computations and resilience in noisy environments. This innovative synergy of SNNs with RL is instrumental in optimizing UAV placement, addressing the burgeoning demand for improved wireless connectivity in a variety of settings. The utilization of non-orthogonal multiple access (NOMA) further underscores the crucial role of UAV base stations in meeting evolving connectivity challenges.