Adaptive Security Solutions for NOMA Networks: The Role of DDPG and RIS-Equipped UAVs

Syed Zain  Ul Abideen; Abdul  Wahid; Mian Muhammad  Kamal

doi:10.31258/ijeepse.7.3.158-174

Syed Zain Ul Abideen College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Abdul Wahid College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
Mian Muhammad Kamal School of Electronic Science and Engineering, Southeast University, Nanjing, China

DOI: https://doi.org/10.31258/ijeepse.7.3.158-174

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Keywords: non-orthogonal multiple access, reconfigurable intelligent surface, unmanned aerial vehicles, deep reinforcement learning, deep deterministic policy gradient, Physical layer security.

Abstract

In this work, we apply the Deep Deterministic Policy Gradient (DDPG) technique to improve the security of a non-orthogonal multiple access (NOMA) downlink network by enabling use of a reconfigurable intelligent surface (RIS) equipped unmanned aerial vehicles (UAV). Our main objective is to prevent eavesdroppers from accessing the network while preserving seamless communication for authorized users. The system is made up of a UAV integrated with an RIS which is essential for optimizing signal paths, and a Base Station. Our work aims to maximize secrecy rates for all users under possible eavesdropping scenarios by dynamically adjusting the RIS’s phase shifts and power allocations. This method not only shows how flexible and successful the DDPG algorithm is at protecting wireless communications when used in conjunction with an RIS but it also highlights how much the algorithm has advanced secure communication systems.

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