Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks Article Swipe
Ahrar N. Hamad
,
Ahmad Adnan Qidan
,
Taisir E. H. El-Gorashi
,
Jaafar M. H. Elmirghani
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.04842
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.04842
Optical wireless communication (OWC) is envisioned as one of the main enabling technologies of 6G networks, complementing radio frequency (RF) systems to provide high data rates. One of the crucial issues in indoor OWC is service interruptions due to blockages that obstruct the line of sight (LoS) between users and their access points (APs). Recently, reflecting surfaces referred to as intelligent reflecting surfaces (IRSs) have been considered to provide improved connectivity in OWC systems by reflecting AP signals toward users. In this study, we investigate the integration of IRSs into an indoor OWC system to improve the sum rate of the users and to ensure service continuity. We formulate an optimization problem for sum rate maximization, where the allocation of both APs and mirror elements of IRSs to users is determined to enhance the aggregate data rate. Moreover, reinforcement learning (RL) algorithms, specifically Q-learning and SARSA algorithms, are proposed to provide real-time solutions with low complexity and without prior system knowledge. The results show that the RL algorithms achieve near-optimal solutions that are close to the solutions of mixed integer linear programming (MILP). The results also show that the proposed scheme achieves up to a 45% increase in data rate compared to a traditional scheme that optimizes only the allocation of APs while the mirror elements are assigned to users based on the distance.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.04842
- https://arxiv.org/pdf/2409.04842
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403600114
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4403600114Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.04842Digital Object Identifier
- Title
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Reinforcement Learning for Rate Maximization in IRS-aided OWC NetworksWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-07Full publication date if available
- Authors
-
Ahrar N. Hamad, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. ElmirghaniList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.04842Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2409.04842Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.04842Direct OA link when available
- Concepts
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Reinforcement learning, Reinforcement, Maximization, Computer science, Mathematical optimization, Artificial intelligence, Mathematics, Psychology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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