Energy optimization algorithm for ISAC-enabled unmanned aerial vehicles system via reinforcement learning
Integrated sensing and communication(ISAC)technology combined with the cost-effective and flexibly controllable unmanned aerial vehicle(UAV)becomes the potential technology to enable a variety of applications for the future"Internet of Everything"in the sixth generation(6G)communication system.To reduce the energy consumption of ISAC-enabled UAV systems,a joint optimization algorithm based on reinforcement learning(RL)is proposed to design UAV's trajectory and allocate the transmit power.Under constraints of user communication rate and the target sensing beam pattern gain,this algorithm can achieve intelligent decision-making for UAV trajectory and power allocation by constructing a linearly weighted reward function related to UAV energy consumption,transmit beamforming pattern gain,and communication rate.The simulation results indicate that the proposed scheme can reduce energy consumption by 12.36%to 21.08%in comparison to the benchmark schemes.Furthermore,the proposed scheme demonstrates superior convergence.
integrated sensing and communicationunmanned aerial vehiclereinforcement learningenergy optimization
万方数据
维普
