Machine Learning-Driven Adaptive Power Allocation for Optical Wireless Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914192047472640 |
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| author | Ncube, Walter Zibusiso Qidan, Ahmad Adnan El-Gorashi, Taisir Elmirghani, Jaafar M. H. |
| author_facet | Ncube, Walter Zibusiso Qidan, Ahmad Adnan El-Gorashi, Taisir Elmirghani, Jaafar M. H. |
| contents | Vertical Cavity Surface Emitting Lasers (VCSELs) have gained popularity in Optical Wireless Communication (OWC) due to their high modulation bandwidth, narrow spectral width, and directional beam, offering improved spectral efficiency and reduced multipath dispersion compared to Light Emitting Diodes (LEDs). In this work, we explore the deployment of VCSELs as Access Points (APs) in an indoor environment under mobility and time varying user distributions. To enhance performance, a Merged Access Point (MAP) topology is introduced to extend the serving area of each cell, whilst Zero Forcing (ZF) precoding is employed for inter user interference management. A sum rate maximisation problem is then formulated to maintain high quality network operation in the dynamic environment. Although deterministic methods can solve the formulated problem, they become impractical in real time due to computational complexity, particularly under high user mobility and rapidly changing channel conditions. To address this, we propose a hybrid Machine Learning (ML) based solution combining a low complexity distance based user association algorithm with a Convolutional Neural Network (CNN) for adaptive power allocation. Simulation results show that the proposed hybrid association CNN framework achieves near optimal performance while substantially reducing computation complexity relative to optimisation based schemes. Furthermore, it operates in real time, with measured median and P95 inference latencies in the millisecond range, and maintains a small empirical worst-case gap to the Mixed Integer Linear Programming (MILP) optimum, demonstrating both practicality and robustness under mobility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Driven Adaptive Power Allocation for Optical Wireless Networks Ncube, Walter Zibusiso Qidan, Ahmad Adnan El-Gorashi, Taisir Elmirghani, Jaafar M. H. Signal Processing Vertical Cavity Surface Emitting Lasers (VCSELs) have gained popularity in Optical Wireless Communication (OWC) due to their high modulation bandwidth, narrow spectral width, and directional beam, offering improved spectral efficiency and reduced multipath dispersion compared to Light Emitting Diodes (LEDs). In this work, we explore the deployment of VCSELs as Access Points (APs) in an indoor environment under mobility and time varying user distributions. To enhance performance, a Merged Access Point (MAP) topology is introduced to extend the serving area of each cell, whilst Zero Forcing (ZF) precoding is employed for inter user interference management. A sum rate maximisation problem is then formulated to maintain high quality network operation in the dynamic environment. Although deterministic methods can solve the formulated problem, they become impractical in real time due to computational complexity, particularly under high user mobility and rapidly changing channel conditions. To address this, we propose a hybrid Machine Learning (ML) based solution combining a low complexity distance based user association algorithm with a Convolutional Neural Network (CNN) for adaptive power allocation. Simulation results show that the proposed hybrid association CNN framework achieves near optimal performance while substantially reducing computation complexity relative to optimisation based schemes. Furthermore, it operates in real time, with measured median and P95 inference latencies in the millisecond range, and maintains a small empirical worst-case gap to the Mixed Integer Linear Programming (MILP) optimum, demonstrating both practicality and robustness under mobility. |
| title | Machine Learning-Driven Adaptive Power Allocation for Optical Wireless Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.04410 |