An Improved Reinforcement Learning-Based 6G UAV Communication for Smart Cities

Unmanned Aerial Vehicles (UAVs) have become integral components in smart city infrastructures, supporting applications such as emergency response, surveillance, and data collection. However, the high mobility and dynamic topology of Flying Ad Hoc Networks (FANETs) present significant challenges for maintaining reliable, low-latency communication. Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable. To overcome these limitations, this paper proposes an improved routing protocol based on reinforcement learning. This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware. The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption, delay, and link quality. Additionally, a Kalman filter is integrated to predict UAV mobility, improving the stability of communication links under dynamic network conditions. Simulation experiments were conducted using realistic scenarios, varying the number of UAVs to assess scalability. An analysis was conducted on key performance metrics, including the packet delivery ratio, end-to-end delay, and total energy consumption. The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15% and reduces delay by up to 25.5% when compared to conventional GEO and QGEO protocols. However, this improvement comes at the cost of higher energy consumption due to additional computations and control overhead. Despite this trade-off, the proposed solution ensures reliable and efficient communication, making it well-suited for large-scale UAV networks operating in complex urban environments.