Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks
Existing literature studying the access point (AP)-user association problem of heterogeneous radio-optical networks either investigates quasi-static network selection or only considers vertical handover (VHO) dwell time from optical to radio. The quasi-static assumption can result in outdated decisi...
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IEEE
2020-01-01
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| Series: | IEEE Photonics Journal |
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| author | Sihua Shao Guanxiong Liu Abdallah Khreishah Moussa Ayyash Hany Elgala Thomas D. C. Little Michael Rahaim |
| author_facet | Sihua Shao Guanxiong Liu Abdallah Khreishah Moussa Ayyash Hany Elgala Thomas D. C. Little Michael Rahaim |
| author_sort | Sihua Shao |
| collection | DOAJ |
| description | Existing literature studying the access point (AP)-user association problem of heterogeneous radio-optical networks either investigates quasi-static network selection or only considers vertical handover (VHO) dwell time from optical to radio. The quasi-static assumption can result in outdated decisions for highly mobile scenarios. Solely focusing on the optical to radio handover ignores the importance of dwell time for VHO from radio to optical. In this paper, we propose a flexible and holistic framework, that runs a self-optimizing algorithm at the centralized coordinator (CC). This CC resides in the LTE eNodeB and controls the handover parameters of all the visible light communication (VLC) APs under the coverage of the LTE eNodeB. Based on Q-learning approach, the algorithm optimizes the time-to-trigger (<inline-formula><tex-math notation="LaTeX">$TTT$</tex-math></inline-formula>) values for VHO between LTE and VLC. Case studies are performed to validate the considerable gain in terms of average throughput by optimizing <inline-formula><tex-math notation="LaTeX">$TTT$</tex-math></inline-formula>s. We evaluate the impact of learning parameters on the optimal throughput and convergence speed through trace-driven simulations. The simulation results reveal that the Q-learning based algorithm improves the average throughput of mobile device by 25% when compared to the fixed <inline-formula><tex-math notation="LaTeX">$TTT$</tex-math></inline-formula> scheme. Furthermore, this algorithm is capable of self-optimizing handover parameters in an online manner. |
| format | Article |
| id | doaj-art-7ddc8e8a9ea240ed9d9dfbd55e9e7091 |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-7ddc8e8a9ea240ed9d9dfbd55e9e70912025-08-20T03:33:14ZengIEEEIEEE Photonics Journal1943-06552020-01-0112111510.1109/JPHOT.2019.29538638903258Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical NetworksSihua Shao0https://orcid.org/0000-0002-2831-9860Guanxiong Liu1Abdallah Khreishah2https://orcid.org/0000-0003-1583-713XMoussa Ayyash3https://orcid.org/0000-0003-0868-143XHany Elgala4https://orcid.org/0000-0002-7098-9278Thomas D. C. Little5https://orcid.org/0000-0001-5208-5358Michael Rahaim6Department of Electrical Engineering, New Mexico Tech, Socorro, NM, USADepartment of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USADepartment of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USADepartment of Computer and Mathematical Sciences, Lewis University, Romeoville, IL, USADepartment of Electrical and Computer Engineering, SUNY at Albany, Albany, NY, USADepartment of Electrical and Computer Engineering, Boston University, MA, USADepartment of Engineering, University of Massachusetts Boston, MA, USAExisting literature studying the access point (AP)-user association problem of heterogeneous radio-optical networks either investigates quasi-static network selection or only considers vertical handover (VHO) dwell time from optical to radio. The quasi-static assumption can result in outdated decisions for highly mobile scenarios. Solely focusing on the optical to radio handover ignores the importance of dwell time for VHO from radio to optical. In this paper, we propose a flexible and holistic framework, that runs a self-optimizing algorithm at the centralized coordinator (CC). This CC resides in the LTE eNodeB and controls the handover parameters of all the visible light communication (VLC) APs under the coverage of the LTE eNodeB. Based on Q-learning approach, the algorithm optimizes the time-to-trigger (<inline-formula><tex-math notation="LaTeX">$TTT$</tex-math></inline-formula>) values for VHO between LTE and VLC. Case studies are performed to validate the considerable gain in terms of average throughput by optimizing <inline-formula><tex-math notation="LaTeX">$TTT$</tex-math></inline-formula>s. We evaluate the impact of learning parameters on the optimal throughput and convergence speed through trace-driven simulations. The simulation results reveal that the Q-learning based algorithm improves the average throughput of mobile device by 25% when compared to the fixed <inline-formula><tex-math notation="LaTeX">$TTT$</tex-math></inline-formula> scheme. Furthermore, this algorithm is capable of self-optimizing handover parameters in an online manner.https://ieeexplore.ieee.org/document/8903258/HandoverQ-learningheterogeneous networkvisible light communicationparameter optimization. |
| spellingShingle | Sihua Shao Guanxiong Liu Abdallah Khreishah Moussa Ayyash Hany Elgala Thomas D. C. Little Michael Rahaim Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks IEEE Photonics Journal Handover Q-learning heterogeneous network visible light communication parameter optimization. |
| title | Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks |
| title_full | Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks |
| title_fullStr | Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks |
| title_full_unstemmed | Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks |
| title_short | Optimizing Handover Parameters by Q-Learning for Heterogeneous Radio-Optical Networks |
| title_sort | optimizing handover parameters by q learning for heterogeneous radio optical networks |
| topic | Handover Q-learning heterogeneous network visible light communication parameter optimization. |
| url | https://ieeexplore.ieee.org/document/8903258/ |
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