بهبود فرآیند انتقال بین سلول‌ها با استفاده از الگوریتم‌های یادگیری ماشین و پارامترهای قدرت سیگنال دریافتی
محورهای موضوعی : فناوری های نوین در سیستم های توزیع شده و محاسبات الگوریتمی
بابک لطفی
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یاسر علمی سولا
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1 - گروه مهندسی کامپیوتر و فناوری اطلاعات،دانشگاه آزاد سبزوار، سبزوار، ایران
2 - گروه مهندسی کامپیوتر و فناوری اطلاعات،دانشگاه آزاد سبزوار، سبزوار، ایران
کلید واژه: الگوریتم‌های یادگیری ماشین, انتقال بین سلولی, خوشه‌بندی, شبکه‌های نسل پنجم, قدرت سیگنال دریافتی, کیفیت خدمات,
چکیده مقاله :
در شبکه‌های نسل پنجم (5G)، کاهش اندازه سلول‌ها منجر به افزایش چشمگیر تعداد فرآیندهای انتقال بین سلولی (Handover) شده است. با توجه به تأثیر مستقیم این فرآیند بر کیفیت خدمات (QoS) و تجربه کاربران، مدیریت کارآمد آن از اهمیت ویژه‌ای برخوردار است. در این راستا، پژوهش حاضر با استفاده از الگوریتم‌های یادگیری ماشین و ترکیب سه روش خوشه‌بندی K-means، DBSCAN وGMM، به بهبود فرآیند انتقال پرداخته است. در روش پیشنهادی، با اصلاح ویژگی‌های مورداستفاده در تحقیقات پیشین، تلاش شده است تا کارایی شبکه بهینه‌سازی شده و تعداد انتقال‌های غیرضروری کاهش یابد. الگوریتم K-Means به دلیل سادگی و کارایی بالا، جهت کاهش واریانس درون‌خوشه‌ای استفاده شده، اما چالش‌هایی مانند حساسیت به نویز و نیاز به تعیین تعداد خوشه‌ها دارد. در مقابل، DBSCAN بدون وابستگی به تعداد خوشه‌ها، بر اساس چگالی نقاط عمل کرده و قابلیت حذف داده‌های نویزی را دارد. همچنین، الگوریتم GMM با مدل‌سازی آماری پیچیده، خوشه‌بندی دقیق‌تری را فراهم می‌کند. در این پژوهش، پس از پیش‌پردازش ویژگی‌های موقعیت کاربران، قدرت سیگنال دریافتی و وضعیت سلول‌ها، ترکیب این سه الگوریتم در سه روش موازی، سری و ترکیبی جهت تصمیم‌گیری بهینه در فرآیند انتقال بررسی شده است. به‌کارگیری این رویکرد تطبیقی نه‌تنها موجب کاهش تعداد انتقال‌ها و اثرات منفی ناشی از آن شده، بلکه کیفیت ارتباطات و توان عملیاتی شبکه را نیز بهبود داده است. نتایج پیاده‌سازی نشان می‌دهد که روش پیشنهادی می‌تواند تجربه کاربری بهتری را در شبکه‌های متراکم نسل پنجم، فراهم کند.
In 5G networks, the reduction in cell size has significantly increased the frequency of handover processes. Given the direct impact of this process on quality of service (QoS) and user experience, efficient handover management is of critical importance. This study focuses on improving the handover process using machine learning algorithms and a combination of three clustering methods: K-means, DBSCAN, and GMM. The proposed approach refines the features used in previous studies to optimize network performance and reduce unnecessary handovers. The K-means algorithm, known for its simplicity and efficiency, minimizes intra-cluster variance, yet it faces challenges such as sensitivity to noise and the need to predefine the number of clusters. In contrast, DBSCAN, which operates based on point density, does not require a predefined number of clusters and can effectively eliminate noisy data points. Additionally, GMM, through complex statistical modeling, provides more precise clustering and a better representation of data distribution. In this study, after preprocessing user location, received signal strength, and cell status, these three algorithms were integrated using parallel, sequential, and hybrid methods to enhance decision-making in the handover process. This adaptive approach not only reduces the number of handovers and their negative effects but also enhances network efficiency and communication quality. The implementation results indicate that the proposed method can significantly improve user experience in dense 5G networks, making the handover process more reliable and efficient
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