[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127159-en":3,"doc-seo-127159-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127159,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","An Analysis of Machine Learning Applications in Visible Light Communication Systems - Research Review","Visible light communication (VLC) enables high-speed data transmission while providing illumination, yet nonlinear distortions in transceiver processing reduce efficiency and complicate signal handling. Machine learning offers data-driven compensation for these effects across key tasks including channel estimation, jitter suppression, position tracking, modulation detection, phase estimation, and security support. The study surveys machine learning algorithms for indoor VLC system design, evaluates their application challenges, and outlines directions for future research to improve robustness and performance.","An Analysis of Machine Learning Applications in Visible Light Communication Systems  \n[1] Dr. S. Menaka, [2] Dr. Chaitanya Krishnakumar, [3] Mr. P. Sivakumar, [4] Mrs. N. Anandha Priya  \nDepartment of Computer Applications, Nehru Institute of Information Technology and Management, Coimbatore, Tamilnadu,  \nIndia.  \nAbstract : With the growing use of high-bandwidth applications, visible light communication (VLC) has surfaced as a promising method for high-speed data transmission due to its dual capability of providing illumination and data transfer. However, VLC faces significant challenges due to various nonlinear distortions that affect signal processing and reduce system efficiency. Machine learning (ML) techniques offer a valuable approach to mitigating these negative effects of transceiver nonlinearity. ML can be applied to numerous VLC issues, such as channel estimation, jitter compensation, position tracking, modulation detection, phase estimation, and security. This study presents an in-depth review ofvarious ML algorithms aimed at simplifying the design of indoor VLC systems and enhancing their performance. Additionally, it explores different ML applications, associated challenges, and potential future research directions in the context of VLC.  \nKeywords : Visible Light Communication, Machine Learning, Wireless Communication.  \n1. Introduction  \nIn response to the growing need for high data rate transmissions, visible light communication (VLC) has become an increasingly popular area of research. VLC operates in the upper portion of the electromagnetic spectrum, which is free from licensing requirements and experiences minimal interference, while offering high data rates and spectrum efficiency [1, 2] . It is a cost-effective and energyefficient technology, utilizing a spectrum that is 1000 times more efficient than radio frequencies and can concurrently provide both communication and illumination [3] . These benefits make VLC a promising alternative to traditional indoor radio frequency communications. Additionally, VLC is drawing interest for underwater communication applications, which require high-speed and long-distance wireless connectivity [4] .  \nAs technology progresses, managing large volumes of data presents more challenges, imposing constraints on communication networks in terms of bandwidth, latency, and reliability. To address these limitations, communication technologies and architectures have advanced, incorporating  \nimproved modulation techniques, innovative multiplexing methods, and enhanced security features. However, these advancements also add complexity, making systems more difficult to operate and manage [5] . Currently, optical communication systems are static, with a fixed physical channel path between source and destination, which simplifies hardware requirements. Future optical communication systems are anticipated to be dynamic,  \nflexible with spectrum and modulation formats, programmable, and adaptable, which will enhance system performance, flexibility, and efficiency [5] .  \nMachine learning (ML) approaches offer promising solutions for enhancing the intelligence of communication nodes. ML is well-suited for addressing complex problems that traditional methods struggle with or cannot solve. By replacing conventional software with ML techniques, systems can learn from previous data to tackle intricate issues [6]. In wireless communication, ML has advanced to the point where it can enable systems to understand and process information through data interactions. Researchers and engineers globally are exploring how ML protocols can aid in developing 5G standards [7, 8] . Despite the potential synergy between wireless communication and ML, they have often been studied separately. In channel modeling for wireless communication, algorithms based on probability and signal processing can sometimes show inaccuracies, leading to performance assessment errors. ML techniques can detect system flaws wi","cbCaioJ04CC43HRW","https://ap.wps.com/l/cbCaioJ04CC43HRW","pdf",386503,1,11,"English","en",105,"# Introduction\n## Machine learning for intelligent communication nodes\n## ML tasks in wireless communication\n# ML algorithms in VLC systems\n## Nonlinearity, noise, and input-output mapping\n## Positioning and modulation-related methods\n## Phase variation handling\n# Research gap and study contributions","[{\"question\":\"Why is visible light communication considered promising for high-speed indoor networking?\",\"answer\":\"VLC supports high data rates and spectrum efficiency while also enabling illumination without licensing and with minimal interference, making it a strong alternative for indoor wireless communication.\"},{\"question\":\"What main VLC challenge does the study address using machine learning?\",\"answer\":\"The study focuses on significant nonlinear distortions caused by transceiver nonlinearity, which degrade signal processing and system efficiency.\"},{\"question\":\"Which VLC problems can machine learning help with according to the paper?\",\"answer\":\"Machine learning can support channel estimation, jitter compensation, position tracking, modulation detection, phase estimation, and security-related aspects of VLC systems.\"}]","An Analysis of Machine Learning Applications in Visible Light Communication Systems - 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