IEICE TRANSACTIONS on Communications
A Reinforcement Learning Approach for Self-Optimization of Coverage and Capacity in Heterogeneous Cellular Networks
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
Keyword
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Junxuan WANG, Meng YU, Xuewei ZHANG, Fan JIANG, "A Reinforcement Learning Approach for Self-Optimization of Coverage and Capacity in Heterogeneous Cellular Networks" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 10, pp. 1318-1327, October 2021, doi: 10.1587/transcom.2020EBP3118.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020EBP3118/_p
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@ARTICLE{e104-b_10_1318,
author={Junxuan WANG, Meng YU, Xuewei ZHANG, Fan JIANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Reinforcement Learning Approach for Self-Optimization of Coverage and Capacity in Heterogeneous Cellular Networks},
year={2021},
volume={E104-B},
number={10},
pages={1318-1327},
abstract={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.},
keywords={},
doi={10.1587/transcom.2020EBP3118},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - A Reinforcement Learning Approach for Self-Optimization of Coverage and Capacity in Heterogeneous Cellular Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1318
EP - 1327
AU - Junxuan WANG
AU - Meng YU
AU - Xuewei ZHANG
AU - Fan JIANG
PY - 2021
DO - 10.1587/transcom.2020EBP3118
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2021
AB - 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.
ER -
English
