استخدام نموذج ذكاء اصطناعي هجين للتنبؤ بجودة الخدمة (QoS) في شبكات الجيل الخامس (5G) بالاعتماد على معلمات القناة اللاسلكية
محتوى المقالة الرئيسي
الملخص
تقترح هذه الورقة نموذجًا هجينًا للذكاء الاصطناعي (AI) يجمع بين الشبكات العصبية الاصطناعية (ANN) وخوارزمية تحسين سرب الجسيمات (PSO) للتنبؤ بجودة الخدمة (QoS) في شبكات الجيل الخامس (5G). يستخدم النموذج مؤشرات القناة الراديوية مثل قدرة الإشارة المستقبلة المرجعية (RSRP)، وجودة الإشارة المستقبلة المرجعية (RSRQ)، ومؤشر قوة الإشارة المستقبلة (RSSI)، ومؤشر جودة القناة (CQI) للتنبؤ بمستويات معدل النقل (Throughput) وزمن التأخير (Latency). تُعد هذه المؤشرات عوامل حاسمة تؤثر على أداء الشبكة؛ إلا أن العلاقة غير الخطية بينها تجعل النماذج التحليلية التقليدية غير كافية لتحقيق تنبؤ دقيق بجودة الخدمة. تمت مقارنة النموذج الهجين ANN-PSO، الذي يدمج بين الشبكة العصبية الاصطناعية وخوارزمية تحسين سرب الجسيمات، مع طرق تقليدية مثل الانحدار الخطي (Linear Regression) والغابة العشوائية (Random Forest) لتقييم دقة التنبؤ. تُظهر النتائج التجريبية أن النموذج المقترح يحقق دقة أعلى وخطأ تنبؤ أقل، مما يجعله أداة واعدة لتحسين جودة الخدمة بشكل تنبؤي في أنظمة الجيل الخامس الحديثة.
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المراجع
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