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

Öz

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.

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

1. [1] M. Shafi, R. K. Jha, and S. Jain, 6g: Technology evolution in future wireless networks, IEEE Access, vol. 12, pp. 57 548–57 573, 2024.
2. [2] M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, Toward 6g networks: Use cases and technologies, IEEE Communications Magazine, vol. 58, no. 3, pp. 55–61, 2020.
3. [3] S. Gopi, S. Kalyani, and L. Hanzo, Cooperative 3d beamforming for small-cell and cell-free 6g systems, IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5023–5036, 2022.
4. [4] Y. Sadi, S. Erkucuk, and E. Panayirci, Flexible physical layer-based resource allocation for machine type communications towards 6g, in 2020 2nd 6G Wireless Summit (6G SUMMIT), 2020, pp. 1–5.
5. [5] S. Mhatre, F. Adelantado, K. Ramantas, and C. Verikoukis, Aiaas for oran-based 6g networks: Multi-time scale slice resource management with drl, in ICC 2024 - IEEE International Conference on Communications, 2024, pp. 5407–5412.
6. [6] F. Zhou, R. Ding, Q. Wu, D. W. K. Ng, K.-K. Wong, and N. Al-Dhahir,A partially observable deep multi-agent active inference framework for resource allocation in 6g and beyond wireless communications networks, in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2023, pp. 2662–2667.
7. [7] A. Nouruzi, A. Rezaei, A. Khalili, N. Mokari, M. R. Javan, E. A. Jorswieck, and H. Yanikomeroglu, Smart resource allocation model via artificial intelligence in software defined 6g networks, in ICC 2023 - IEEE International Conference on Communications, 2023, pp. 5141–5146.
8. [8] W. Sun, H. Xu, H. Wang, S. Chang, S. Sun, and D. Miao, Research on user-centric wireless resource allocation of is tn based on reinforcement learning, in 2023 IEEE Globecom Workshops (GC Wkshps), 2023, pp. 141–146.

9. [9] D. Li, S. R. Khosravirad, T. Tao, P. Baracca, and P. Wen, Power allocation for 6g sub-networks in industrial wireless control, in 2024 IEEE Wireless Communications and Networking Conference (WCNC), 2024, pp. 1–6.
10. [10] M. Wu, Y. Xiao, Y. Gao, and X. Lei, Design of quality-of-experience criteria for resource allocation toward 6g wireless networks: A review and new directions, in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022, pp. 1–7.
11. [11] X. Shen, W. Liao, and Q. Yin, “A novel wireless resource management for the 6g-enabled high-density internet of things,” IEEE Wireless Communications, vol. 29, no. 1, pp. 32–39, 2022.
12. [12] H. Zhang, Y. Zhang, X. Liu, K. Sun, and Y. Zhang, “Resource allocation and mobility management for perceptive mobile networks in 6g,” IEEE Wireless Communications, vol. 31, no. 4, pp. 223–229, 2024.
13. [13] Q. Guo, F. Tang, and N. Kato, “Federated reinforcement learning-based resource allocation in d2d-enabled 6g,” IEEE Network, vol. 37, no. 5, pp. 89–95, 2023.
14. [14] F. Rezazadeh, H. Chergui, S. Siddiqui, J. Mangues, H. Song, W. Saad, and M. Bennis, “Intelligible protocol learning for resource allocation in 6g o-ran slicing,” IEEE Wireless Communications, vol. 31, no. 5, pp. 192–199, 2024.
15. [15] J. Wang, Y. Li, J. Liu, and N. Kato, “Intelligent network slicing for b5g and 6g: Resource allocation, service provisioning, and security,” IEEE Wireless Communications, vol. 31, no. 3, pp. 271–277, 2024.
16. [16] J. Zhao, Y. Chen, and Y. Huang, “Qoe-driven wireless communication resource allocation based on digital twin edge network,” IEEE Journal of Radio Frequency Identification, vol. 8, pp. 277–281, 2024.
17. [17] D. Gross, J. F. Shortle, J. M. Thompson, and C. M. Harris, Fundamentals of Queueing Theory, 4th ed. USA: Wiley-Interscience, 2008.