Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems
Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| author | Ahmed Al Hammadi Lina Bariah Sami Muhaidat Mahmoud Al-Qutayri Paschalis C. Sofotasios Merouane Debbah |
| author_facet | Ahmed Al Hammadi Lina Bariah Sami Muhaidat Mahmoud Al-Qutayri Paschalis C. Sofotasios Merouane Debbah |
| author_sort | Ahmed Al Hammadi |
| collection | DOAJ |
| description | Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain’s Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance. |
| format | Article |
| id | doaj-art-f02fd8bae6ec460bb50d0ebb6d04e3bd |
| institution | OA Journals |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-f02fd8bae6ec460bb50d0ebb6d04e3bd2025-08-20T02:05:01ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-012344810.1109/TMLCN.2023.332850110299678Deep Q-Learning-Based Resource Management in IRS-Assisted VLC SystemsAhmed Al Hammadi0https://orcid.org/0009-0006-8535-268XLina Bariah1https://orcid.org/0000-0001-7244-1663Sami Muhaidat2https://orcid.org/0000-0003-4649-9399Mahmoud Al-Qutayri3https://orcid.org/0000-0002-9600-8036Paschalis C. Sofotasios4https://orcid.org/0000-0001-8389-0966Merouane Debbah5https://orcid.org/0000-0001-8941-8080Technology Innovation Institute, Abu Dhabi, United Arab EmiratesTechnology Innovation Institute, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering and Computer Science, Center for Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering and Computer Science, Center for Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesVisible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of both the overall SE and Jain’s Fair Index, achieved at a fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise. Our results also highlight the effectiveness of the proposed algorithm in leveraging the increasing number of IRS elements for optimized performance.https://ieeexplore.ieee.org/document/10299678/Visible light communications (VLC)intelligent reflecting surfaces (IRS)deep Q-learning (DQL)spectral efficiency |
| spellingShingle | Ahmed Al Hammadi Lina Bariah Sami Muhaidat Mahmoud Al-Qutayri Paschalis C. Sofotasios Merouane Debbah Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems IEEE Transactions on Machine Learning in Communications and Networking Visible light communications (VLC) intelligent reflecting surfaces (IRS) deep Q-learning (DQL) spectral efficiency |
| title | Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems |
| title_full | Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems |
| title_fullStr | Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems |
| title_full_unstemmed | Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems |
| title_short | Deep Q-Learning-Based Resource Management in IRS-Assisted VLC Systems |
| title_sort | deep q learning based resource management in irs assisted vlc systems |
| topic | Visible light communications (VLC) intelligent reflecting surfaces (IRS) deep Q-learning (DQL) spectral efficiency |
| url | https://ieeexplore.ieee.org/document/10299678/ |
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