期刊
CHINESE JOURNAL OF ELECTRONICS
卷 32, 期 2, 页码 283-294出版社
WILEY
DOI: 10.23919/cje.2021.00.327
关键词
Deep learning; Wireless communication; Base stations; Solid modeling; Three-dimensional displays; Simulation; Interference; Aerial base station; mmWave; Capacity enhancement; Cooperative communication; Multi-agent deep reinforcement learning (MADRL)
This paper introduces coordinated multiple points transmission (CoMP) into the millimeter-wave aerial base station (mAeBS) assisted network to enhance capacity. It proposes a two-timescale approach for three-dimensional (3D) deployment and user association of cooperative mAeBSs. Simulation results show that the proposed approach achieves significant throughput gains and the MADDPG algorithm is more efficient than centralized DRL algorithms in deriving solutions.
Although millimeter-wave aerial base station (mAeBS) gains rich wireless capacity, it is technically difficult for deploying several mAeBSs to solve the surge of data traffic in hotspots when considering the amount of interference from neighboring mAeBS. This paper introduces coordinated multiple points transmission (CoMP) into the mAeBS-assisted network for capacity enhancement and designs a two-timescale approach for three-dimensional (3D) deployment and user association of cooperative mAeBSs. Specially, an affinity propagation clustering based mAeBS-user cooperative association scheme is conducted on a large timescale followed by modeling the capacity evaluation, and a deployment algorithm based on multi-agent (MA) deep deterministic policy gradient (MADDPG) is designed on the small timescale to obtain the 3D position of mAeBS in a distributed manner. Simulation results show that the proposed approach has significant throughput gains over conventional schemes without CoMP, and the MADDPG is more efficient than centralized deep reinforcement learning (DRL) algorithms in deriving the solution.
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