Reinforcement Learning-Based User's Rate Optimizaton Algorithm for UAV-aided Communication Network
Unmanned Aerial Vehicles(UAVs)aid ground cellular base station and form a hybrid communication network,which is expected to become an important means of improving rate of users.As for the communication network with UAV-aided base,Multi-armed Bandits-based Rate Optimization(MBRO)algorithm is proposed First,the joint optimization problem is established,and then the improved K-means clustering algorithm and multi-armed bandit algorithm are respectively used to solve the problem.MBRO algorithm uses K-means clustering algorithm to realize the deployment of UAV,and uses multi-armed bandit algorithm is utilized to complete channel allocation and transmission power allocation of UAV.The simulation results show that compared with the similar benchmark algorithms,MBRO algorithm increases the rate of the user terminal.
unmanned aerial vehiclesbase stationrate of userK-means algorithmmulti-armed bandit
万方数据
维普
