The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Rangkaian heterogen (HetNets) muncul sebagai kaedah yang tidak dapat dielakkan untuk menangani masalah kapasiti rangkaian selular. Disebabkan oleh persekitaran rangkaian yang rumit dan sejumlah besar parameter yang dikonfigurasikan, liputan dan pengoptimuman kapasiti (CCO) merupakan isu yang mencabar dalam rangkaian selular heterogen. Dengan menggabungkan algoritma pengoptimuman diri untuk parameter frekuensi radio (RF) dengan mekanisme kawalan kuasa sel-sel kecil, masalah CCO rangkaian mengatur diri ditangani dalam kertas ini. Pertama, pengoptimuman parameter RF diselesaikan berdasarkan pembelajaran tetulang (RL), di mana stesen pangkalan dimodelkan sebagai ejen yang boleh mempelajari strategi berkesan untuk mengawal parameter boleh tala dengan berinteraksi dengan persekitaran sekeliling. Kedua, sel kecil boleh menukar keadaan penghantaran wayarles secara autonomi dengan membandingkan jaraknya dari peralatan pengguna dengan saiz sel maya. Keputusan simulasi menunjukkan bahawa algoritma yang dicadangkan boleh mencapai prestasi yang lebih baik pada daya pemprosesan pengguna berbanding kaedah konvensional yang berbeza.
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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Salinan
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
Salinan
@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},}
Salinan
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 -