Intelligent Interference Prediction and Interference Avoidance in Drone Green Communications

With limited bandwidth and power constraints, ensuring low latency communication is critical for applications with real-time communication requirements such as autonomous driving. With the development of mobile edge computing and future 6G network research, drone communication is becoming an emergency communication solution for low latency applications. This work examines the green transmission design with low latency constraints, where a drone as a wireless relay can forward signals from a base station (BS). Considering the scenario where the nearby drone interferes with the downlink transmission of the desired drone, a resource optimization scheme including interference location awareness and interference avoidance is proposed. The interference prediction mechanism studied will run on the mobile edge computing entities contained in future 6G networks. The entity uses an artificial neural network (ANN) model to quickly predict the distance between the nearby drone and the desired drone. Based on the predicted results, the desired drone will adjust its own flight altitude to reduce the interference from the nearby drone. Finally, the successive convex approximation algorithm (SCA) is used to optimize the resource allocation of the drone to further control the downlink energy consumption. The simulation results show that the proposed scheme with interference avoidance has better energy efficiency than the baseline scheme.