Spectral reinforcement learning based dynamic routing for unmanned aerial vehicle (UAV) networks

Unmanned Aerial Vehicles (UAVs) have received a lot of interest for their prospective uses in various types of disciplines, including communication, disaster management, surveillance, and military applications. UAV ad-hoc networks enable UAVs to interact wirelessly without a permanent infrastructure, making them suited for many circumstances. Conventional methods require predefining the number of clusters, which can lead to inaccurate results, and existing schemes focus on distance as the key parameter while neglecting UAV connectivity; additionally, traditional algorithms struggle with complex UAV network structures due to varying distances, obstacles, and dynamic configurations, making them unable to adapt to frequent changes in connectivity, signal strength, and network topology. This study proposes a framework that integrates spectral clustering and reinforcement learning to optimize the performance of UAV ad hoc networks. Spectral clustering groups UAVs with similar communication characteristics, such as signal strength and geographic location. Reinforcement learning is then used to optimize the path UAVs take within each clustered group, leading to further improvements in network performance. Our approach effectively adapts to changes in network topology and communication patterns, allowing for optimal performance even in dynamic environments. Experimental results demonstrate the effectiveness of our strategy, achieving a Packet Delivery Ratio (PDR) improvement of approximately 18.42% over k-means routing at high mobility scenarios, with an end-to-end delay reduction of around 40% compared to traditional methods. Additionally, the Network Routing Load (NRL) of our proposed scheme remains consistently below 18%, indicating enhanced efficiency compared to existing protocols, which can reach NRL values of up to 35%. Our approach optimizes the communication efficiency of UAV ad-hoc networks by adopting an optimal route policy, resulting in reduced end-to-end delay and improved packet delivery ratio. The proposed framework offers several advantages over existing methods, including adaptability to changes in network topology and communication patterns, efficient communication, and optimal routing decisions.