Search

The integration of Unmanned Aerial Vehicles (UAVs) into future 6G networks will open new possibilities for applications ranging from surveillance to communication infrastructure maintenance, precision agriculture, and surveying. However, ensuring uninterrupted connectivity for UAVs operating in remote or dynamic environments remains a significant challenge. This paper presents a novel approach to achieving seamless handover for UAVs when transitioning between terrestrial and satellite communication networks. The proposed method in this paper, leverages graph theory and develop a decision-making algorithm to optimise handover decisions, minimizing latency, improving performance, and reducing service disruption. It establishes a comprehensive graph model that represents the dynamic topology of available network nodes, including terrestrial base stations and low earth orbit (LEO) satellites, which adapts in real-time to changes in UAV position and network conditions. The approach incorporates a decision-making algorithm that considers several factors, such as received signal strength (RSS), signal-to-noise ratio (SNR), and elevation angle, to determine the optimal time and location for a handover between terrestrial base stations and satellite links. This ensures a seamless transition between communication links, minimizing service disruption. The performance of this method is evaluated through extensive simulations and comparison with existing solutions demonstrating significant improvements in RSS, SNR, throughput, latency, ping-pongs and enhanced overall UAV connectivity. The proposed graph method-based seamless handover solution represents a crucial advancement in enabling reliable and uninterrupted communication for UAVs operating in remote and challenging environments. By managing handovers between terrestrial and satellite networks, this research contributes to the realisation of the full potential of UAVs in emerging applications, thereby advancing the state-of-the-art in UAV technology.

Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.