6G Ağlarında Dinamik Kaynak Tahsisi için Kuyruk Teorisi ve GBM'nin Entegrasyonu

Abstract

6G ağları, önceki nesillere kıyasla kesintisiz bağlantı ve benzeri görülmemiş gelişmeler vaat ediyor. Küçük hücreler, 6G’nin başarısının temelinde yer alıyor; bu hücreler, baz istasyonlarını kullanıcılara yakın yerleştirerek ağ kapsama alanını iyileştiriyor, gecikmeleri azaltıyor ve kapasiteyi artırıyor. Ancak, 6G ağlarında küçük hücrelerin yoğun bir şekilde konuşlandırılması, özellikle büyük durum ve eylem alanları nedeniyle kaynak tahsisinde önemli zorluklar ortaya çıkarıyor. Daha spesifik olarak, binlerce hücre için kaynak tahsis kararlarının hesaplanması gerekiyor. Ayrıca, yüksek kullanıcı hareketliliği nedeniyle sık sık kaynak yeniden tahsisi yapılması, algoritmik yükü artırıyor. Bu zorluklar, gecikme, veri aktarım hızı ve paket kaybı gibi kritik performans metriklerini olumsuz etkiliyor. Bu zorlukların üstesinden gelmek için bu makale, kuyruk teorisi tabanlı küçük hücre durum belirlemeyi Gradient Boosting Machine (GBM) tahminleriyle entegre eden dinamik bir kaynak tahsis modeli öneriyor. Bu entegre yaklaşım, bant genişliği, hesaplama gücü ve enerji kullanımının zamanında tahsis edilmesini sağlayarak dinamik kaynak ayarlamasını mümkün kılıyor. Simülasyon sonuçları, önerilen yaklaşımın gecikme, veri aktarım hızı ve paket kaybı açısından geleneksel kaynak tahsis yöntemlerine kıyasla etkinliğini ortaya koyuyor.

Keywords

• 6G
• Gradient Boosting Makineleri
• Kaynak Tahsisi
• Kuyruk Teorisi
• Küçük Hücreler

Integrating Queuing Theory and GBM for Dynamic Resource Allocation in 6G Networks

Abstract

6G networks promise seamless connectivity and unprecedented advancements compared to previous generations. Small cells are at the core of 6G's success, which improves network coverage, reduces delays, and increases capacity by placing base stations close to users. However, the dense deployment of small cells in 6G networks introduces significant challenges, particularly in resource allocation due to huge state and action spaces. More specifically, resource allocation decisions must be computed for thousands of cells. Also, frequent resource reallocation due to high user mobility increases the algorithmic overhead. These challenges negatively impact critical performance metrics such as latency, throughput, and packet loss. To address these challenges, this paper proposes a dynamic resource allocation model that integrates queuing-theory-based small cell state determination with Gradient Boosting Machine (GBM) predictions. This integrated approach empowers dynamic resource adjustment by ensuring the timely allocation of bandwidth, computing power, and energy usage. The simulation results demonstrate the effectiveness of the proposed approach in terms of latency, throughput, and packet loss compared to conventional resource allocation methods.

Keywords

• 6G
• Gradient Boosting Machines
• Queuing Theory
• Resource Allocation
• Small Cells

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