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| Title | Reinforcement Learning For Resilient Aerial-Irs Assisted Wireless Communications Networks In The Presence Of Multiple Jammers |
|---|---|
| ID_Doc | 44886 |
| Authors | Tariq Z.U.A.; Baccour E.; Erbad A.; Hamdi M. |
| Year | 2024 |
| Published | IEEE Open Journal of the Communications Society, 5 |
| DOI | http://dx.doi.org/10.1109/OJCOMS.2023.3334489 |
| Abstract | The evolving landscape of beyond 5G and 6G wireless communication systems in smart urban environments faces numerous interference-related challenges posed by legitimate and illicit devices. In this context, Intelligent Reflecting Surfaces (IRS) have emerged as a promising solution to mitigate interference caused by obstacles and unknown jamming devices. Existing techniques mainly focus on mitigating the impact of a single jammer in IRS-assisted communications systems, which affects both stationary and mobile devices. Additionally, these approaches target a single objective, such as minimizing the energy, enhancing the transmission rate, or maximizing the Signal-to-Interference-plus-Noise Ratio (SINR), which restrains the performance of the system. This paper offers a comprehensive anti-jamming solution for securing wireless communications in a smart city urban environment comprising diverse public events such as sporting events, parades, festivals, and exhibitions. The focus is on maintaining essential services like security, law enforcement, logistics, emergency response, crowd management, and public health. We introduce a Reinforcement Learning-based technique for UAV-mounted IRS, optimizing trajectory and phase shift beamforming to counteract the disruptive impact of jammers, ensuring reliable communication in dynamic, security-sensitive settings Our approach also seeks to achieve multi-objective optimization by striking a balance between transmission rate and energy consumption in this highly challenging environment. The formulated optimization is computationally complex due to its combinatorial nature. Hence, we leverage the light-weight Deep Reinforcement Learning (DRL) technique called Deep Deterministic Policy Gradient (DDPG) to optimize trajectory and IRS phase shifts and achieve multiple objectives jointly. Experimental results demonstrate the effectiveness of our proposed DDPG-based approach in outperforming other RL algorithms. It achieves a near-optimal solution compared to the benchmark technique within the close gap and improves both achievable transmission rates and energy efficiency compared to related works by 50-70%. © 2020 IEEE. |
| Author Keywords | anti-jamming; DDPG; deep deterministic policy gradient; intelligent reflecting surfaces; IRS; jamming; re-configurable intelligent surfaces; Reinforcement learning; RIS; wireless communications |
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