Blockchain-Empowered Resource Allocation in Multi-UAV-Enabled 5G-RAN: A Multi-agent Deep Reinforcement Learning Approach

Authors

Abegaz Mohammed Seid, Hamad Bin Khalifa University, College of Science and Engineering
Aiman Erbad, Hamad Bin Khalifa University, College of Science and Engineering
Hayla Nahom Abishu, University of Electronic Science and Technology of ChinaFollow
Abdullatif Albaseer, Hamad Bin Khalifa University, College of Science and EngineeringFollow
Mohammed Abdallah, Hamad Bin Khalifa University, College of Science and EngineeringFollow
Mohsen Guizani, Mohamed Bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Cognitive Communications and Networking

Abstract

In 5G and B5G networks, real-time and secure resource allocation with the common telecom infrastructure is challenging. This problem may be more severe when mobile users are growing and connectivity is interrupted by natural disasters or other emergencies. To address the resource allocation problem, the network slicing technique has been applied to assign virtualized resources to multiple network slices, guaranteeing the 5G-RAN quality of service. Moreover, to tackle connectivity interruptions during emergencies, UAVs have been deployed as airborne base stations, providing various services to ground networks. However, this increases the complexity of resource allocation in the shared infrastructure of 5G-RAN. Therefore, this paper proposes a dynamic resource allocation framework that synergies blockchain and multi-agent deep reinforcement learning for multi-UAV-enabled 5G-RAN to allocate resources to smart mobile user equipment (SMUE) with optimal costs. The blockchain ensures the security of virtual resource transactions between SMUEs, infrastructure providers (InPs), and virtual network operators (VNOs). We formulate a virtualized resource allocation problem as a hierarchical Stackelberg game containing InPs, VNOs, and SMUEs, and then transform it into a stochastic game model. Then, we adopt a Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve the formulated problem and obtain the optimal resource allocation policies that maximize the utility function. The simulation results show that the MADDPG method outperforms the state-of-the-art methods in terms of utility optimization and quality of service satisfaction.

First Page

1

Last Page

1

DOI

10.1109/TCCN.2023.3262242

Publication Date

3-27-2023

Keywords

5G mobile communication, 5G-RAN, Blockchain, Blockchains, Games, III-V semiconductor materials, Indium phosphide, Multi-agent DRL, Network slicing, Resource management, Security, Stackelberg game, Virtualization

Comments

IR Deposit conditions:

OA version (pathway a) Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Recommended Citation

A. M. Seid, A. Erbad, H. N. Abishu, A. Albaseer, M. Abdallah and M. Guizani, "Blockchain-Empowered Resource Allocation in Multi-UAV-Enabled 5G-RAN: A Multi-agent Deep Reinforcement Learning Approach," in IEEE Transactions on Cognitive Communications and Networking, pp. 1-1, Mar 2023, doi: 10.1109/TCCN.2023.3262242.