@article{5e37b00a9e15482fa2fd672ec2936489,
title = "Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach",
abstract = "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. ",
keywords = "Anti-jamming, beamforming, intelligent reflecting surface, power allocation, reinforcement learning",
author = "Helin Yang and Zehui Xiong and Jun Zhao and Dusit Niyato and Qingqing Wu and Poor, {H. Vincent} and Massimo Tornatore",
note = "Funding Information: Manuscript received April 26, 2020; revised August 27, 2020; accepted November 9, 2020. Date of publication November 19, 2020; date of current version March 10, 2021. This work was supported in part by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, under Grant NRF2017EWT-EP003-041 and Grant Singapore NRF2015-NRF-ISF001-2277; in part by the Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure under Grant NSoE DeST-SCI2019-0007; in part by the A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing under Grant RGANS1906; in part by the Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (WASP/NTU) under Grant M4082187 (4080); in part by the Singapore Ministry of Education (MOE) Tier 1 under Grant RG16/20; in part by Alibaba Group through the Alibaba Innovative Research (AIR) Program, Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University (NTU) Startup Grant, Singapore Ministry of Education Academic Research Fund under Grant Tier 1 RG128/18, Grant Tier 1 RG115/19, Grant Tier 1 RT07/19, Grant Tier 1 RT01/19, and Grant Tier 2 MOE2019-T2-1-176; in part by the NTU-WASP Joint Project, Singapore National Research Foundation under its Strategic Capability Research Centers Funding Initiative: Strategic Centre for Research in Privacy-Preserving Technologies & Systems, Energy Research Institute @NTU, Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure under Grant NSoE DeST-SCI2019-0012; and in part by the AI Singapore 100 Experiments (100E) programme, NTU Project for Large Vertical Take-Off & Landing Research Platform. This article is to be presented at the 2020 IEEE Global Communications Conference, Taipei, Taiwan. The associate editor coordinating the review of this article and approving it for publication was X. Chen. (Corresponding author: Jun Zhao.) Helin Yang, Zehui Xiong, Jun Zhao, and Dusit Niyato are with the School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798 (e-mail: hyang013@e.ntu.edu.sg; zxiong002@e.ntu.edu.sg; junzhao@ntu.edu.sg; dniyato@ntu.edu.sg). Publisher Copyright: {\textcopyright} 2002-2012 IEEE.",
year = "2021",
month = mar,
doi = "10.1109/TWC.2020.3037767",
language = "English (US)",
volume = "20",
pages = "1963--1974",
journal = "IEEE Transactions on Wireless Communications",
issn = "1536-1276",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",
}