Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks

Nouruzi A, Rezaei A, Khalili A, Mokari N, Javan MR, Jorswieck EA, Yanikomeroglu H (2023)

Publication Type: Conference contribution

Publication year: 2023

Journal

2012 IEEE International Conference on Communications (ICC) Institute of Electrical and Electronics Engineers (IEEE)

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2023-May

Pages Range: 5141-5146

Conference Proceedings Title: IEEE International Conference on Communications

Event location: Rome IT

ISBN: 9781538674628

DOI: 10.1109/ICC45041.2023.10279230

Abstract

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

Authors with CRIS profile

Ataollah Khalilimahmoudabadi Lehrstuhl für Digitale Übertragung

Involved external institutions

Tarbiat Modares University IR Iran, Islamic Republic of (IR) Technische Universität Berlin DE Germany (DE) University of Shahrood / Shahrood University of Technology / دانشگاه صنعتی شاهرود IR Iran, Islamic Republic of (IR) Technische Universität Braunschweig DE Germany (DE) Carleton University CA Canada (CA)

How to cite

APA:

Nouruzi, A., Rezaei, A., Khalili, A., Mokari, N., Javan, M.R., Jorswieck, E.A., & Yanikomeroglu, H. (2023). Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks. In Michele Zorzi, Meixia Tao, Walid Saad (Eds.), IEEE International Conference on Communications (pp. 5141-5146). Rome, IT: Institute of Electrical and Electronics Engineers Inc..

MLA:

Nouruzi, Ali, et al. "Smart Resource Allocation Model via Artificial Intelligence in Software Defined 6G Networks." Proceedings of the 2023 IEEE International Conference on Communications, ICC 2023, Rome Ed. Michele Zorzi, Meixia Tao, Walid Saad, Institute of Electrical and Electronics Engineers Inc., 2023. 5141-5146.

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