As unmanned aerial vehicles (UAVs) are employed in various areas of modern communication, cellular communication with UAV assistance continues to gain attention. However, it can be significantly complex to optimize a system that considers both the link direction between UAVs and ground users, as well as the location of UAVs. Therefore, we introduce a novel hybrid throughput optimization strategy to address the tightly coupled problem involving adjustments to both UAV height and air-ground link direction. First, the link direction is represented as a table of boolean values and optimized within a discrete space with the heuristic algorithm, where the UAV location remains fixed. Second, we optimize the UAV height with a fixed link direction through the deep reinforcement learning method, which can strengthen the robustness of the algorithm. Finally, the desired result is achieved with a hybrid iteration of the previous two processes. Experimental results demonstrate the effectiveness of the proposed algorithm by achieving a 40% gain on the throughput of the transmission link.
UAV-Assisted Hybrid Throughput Optimization Based on Deep Reinforcement Learning
2023-10-13
2785786 byte
Conference paper
Electronic Resource
English