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A Reinforcement Learning Approach for Self-Optimization of Coverage and Capacity in Heterogeneous Cellular Networks

Junxuan WANG, Meng YU, Xuewei ZHANG, Fan JIANG

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Summary :

Heterogeneous networks (HetNets) are emerging as an inevitable method to tackle the capacity crunch of the cellular networks. Due to the complicated network environment and a large number of configured parameters, coverage and capacity optimization (CCO) is a challenging issue in heterogeneous cellular networks. By combining the self-optimizing algorithm for radio frequency (RF) parameters with the power control mechanism of small cells, the CCO problem of self-organizing network is addressed in this paper. First, the optimization of RF parameters is solved based on reinforcement learning (RL), where the base station is modeled as an agent that can learn effective strategies to control the tunable parameters by interacting with the surrounding environment. Second, the small cell can autonomously change the state of wireless transmission by comparing its distance from the user equipment with the virtual cell size. Simulation results show that the proposed algorithm can achieve better performance on user throughput compared to different conventional methods.

Publication
IEICE TRANSACTIONS on Communications Vol.E104-B No.10 pp.1318-1327
Publication Date
2021/10/01
Publicized
2021/04/13
Online ISSN
1745-1345
DOI
10.1587/transcom.2020EBP3118
Type of Manuscript
PAPER
Category
Antennas and Propagation

Authors

Junxuan WANG
  Xi'an University of Post and Telecommunications
Meng YU
  Xi'an University of Post and Telecommunications
Xuewei ZHANG
  Xi'an University of Post and Telecommunications
Fan JIANG
  Xi'an University of Post and Telecommunications

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