رتبه بندی و تخمین سلولهای معیوب با تکنیک تصمیم گیری چند شاخصه و یادگیری ماشین در شبکه های نسل پنج و بالاتر ارتباطات سیار
محورهای موضوعی : مجله فناوری اطلاعات در طراحی مهندسی
رضا معمریامی
1
,
علی اکبر خزاعی
2
,
سعید راحتی قوچانی
3
1 - گروه برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
2 - گروه برق، واحد مشهد، دانشگاه آزاد اسلامی، مشهد، ایران
3 - گروه مهندسی پزشکی، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: تصمیم گیری چند شاخصه, شبکه های خود سازمانده, خودترمیمی سلولهای معیوب, مدیریت سلولهای معیوب, تاپسیس,
چکیده مقاله :
هوشمندی شبکهها 5G&B5G در حوزه مدیریت و پیکربندی، بهینهسازی و خودترمیمی با هدف ، کاهش هزینه های عملیاتی و هزینه های سرمایه ای بدون دخالت نیروی انسانی از الزامات 5G&B5Gاست . فرایند خودترمیمی بهعنوان یکی از زیرمجموعههای اصلی این شبکه ها, به تشخیص سلولهای معیوب و خارج از سرویس و همچنین جبرانسازی این سلول ها می پردازد.در این پژوهش از الگوریتمی یادگیری ترکیبی تحت عنوان طبقه بند پشته ای که یکی از انواع روشهای ترکیبی در مدلهای یادگیری گروهی میباشد، برای تخمین سلولهای معیوب درون شبکه استفاده شده است. بدینمنظور در ابتدا با استفاده از الگوریتم تاپسیس که رویکردی قانونمحوراست در بخش آمادهسازی دادگان به برچسبگذاری مجموعه داده گان و رتبه بندی سلول هاشبکه پرداختهشدهاست , سپس با استفاده از الگوریتم طبقه بند پشته ای طبقهبندی سلولهای معیوب و تخمین در سطح دادگان صورت گرفت. نتایج حاصل از بهکارگیری الگوریتم پیشنهادی ارائهشده نشاندهنده کارایی بالای آن در تشخیص سلولهای معیوب بود. بهطوری معیارهای [1]دقت و [2]صحت و[3]پوشش برای بخشهای آموزش و آزمون مدل یادگیری بیان کننده نرخ بالای تشخیص صحیح در تعیین کلاس هدف بودند.
The intelligence of 5G&B5G networks in the field of management and configuration, optimization and self-healing to reduce operational costs and capital costs without human intervention is one of the requirements of 5G&B5G. The self-healing process, as one of the main sub-sets of these networks, identifies faulty and out-of-service cells and compensates for these cells. In this research, a hybrid learning algorithm called stack classification, which is one of the types of hybrid methods in group learning models, has been used to estimate the faulty cells of the network. For this purpose, first, using the TOPSIS algorithm, which is a rule-based approach, in the data preparation section, data set labeling and grid cell ranking were performed. Then, using the stack classification algorithm, the faulty cells were classified and estimated at the data level. The results of applying the proposed algorithm showed its high efficiency in detecting faulty cells. Therefore, the, precision , recall ,accuracy of the training and testing parts of the learning model showed a high rate of correct detection in determining the target class.
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