4.7 Article

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach

期刊

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 20, 期 3, 页码 1963-1974

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3037767

关键词

Jamming; Array signal processing; Wireless communication; Optimization; Communication systems; Relays; Quality of service; Anti-jamming; intelligent reflecting surface; power allocation; beamforming; reinforcement learning

资金

  1. National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience [NRF2017EWT-EP003-041]
  2. National Research Foundation (NRF), Singapore, under Grant Singapore [NRF2015-NRF-ISF001-2277]
  3. Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure under Grant NSoE [DeST-SCI2019-0007]
  4. A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing [RGANS1906]
  5. Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (WASP/NTU) [M4082187 (4080)]
  6. Singapore Ministry of Education (MOE) Tier 1 [RG16/20]
  7. Alibaba Group through the Alibaba Innovative Research (AIR) Program
  8. Alibaba-NTU Singapore Joint Research Institute (JRI)
  9. Nanyang Technological University (NTU) Startup Grant
  10. Singapore Ministry of Education Academic Research Fund [RG128/18, RG115/19, RT07/19, RT01/19, MOE2019-T2-1-176]
  11. NTU-WASP Joint Project
  12. Singapore National Research Foundation under its Strategic Capability Research Centers Funding Initiative: Strategic Centre for Research in Privacy-Preserving Technologies Systems
  13. Energy Research Institute @NTU
  14. Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure [DeST-SCI2019-0012]
  15. AI Singapore 100 Experiments (100E) programme, NTU Project for Large Vertical Take-Off & Landing Research Platform

向作者/读者索取更多资源

This study focuses on utilizing intelligent reflecting surfaces to mitigate malicious interference caused by smart jammers and enhance communication performance by optimizing power allocation and reflecting beamforming. The proposed fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning method efficiently improves the effectiveness of anti-jamming power allocation and reflecting beamforming strategies.
Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF-CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据