Dynamic Channel Allocation for Multi-UAVs: A Deep Reinforcement Learning Approach

It has been recognized that fixed spectrum and channel allocation will lead to waste of spectrum resources when multiple agents communicate at the same time. Dynamic allocation of channels is proposed to maximize the utilization of spectrum resources. In the environment of multiple unmanned aerial vehicles (UAVs), it is necessary to ensure that each UAV can communicate successfully without interfering with other UAVs. Dynamic allocation of channels plays an important role in such systems. In this paper, we propose a dynamic channel allocation scheme based on deep reinforcement learning for multi-UAV systems. A slotted time system is used by all the UAVs. Di2642erent from the traditional method, the occupancy of each channel is scanned first in each time slot. Then a channel will be selected for data transmission, with feedback from the environment when the transmission is over. The proposed channel allocation scheme incorporates a long short-term memory (LSTM) into the deep reinforcement learning framework, to better learn from the past experience and better adapt to the the highly dynamic environment in a multi-UAV system. The experimental results show that compared with the traditional reinforcement learning method (Q- learning and Deep Q Network (DQN)), the proposed method achieves faster convergence and better performance with respect to average collision rate, average reward, and average successful communication rate.