6 research outputs found
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning
Malicious jamming launched by smart jammer, which attacks legitimate
transmissions has been regarded as one of the critical security challenges in
wireless communications. Thus, this paper exploits 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 smart jammer, an
optimization problem for jointly optimizing power allocation at the base
station (BS) and reflecting beamforming at the IRS is formulated. As the
jamming model and jamming behavior are dynamic and unknown, a win or learn fast
policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly
optimize the anti-jamming power allocation and reflecting beamforming strategy
without the knowledge of the jamming model. Simulation results demonstrate that
the proposed anti-jamming based-learning approach can efficiently improve both
the IRS-assisted system rate and transmission protection level compared with
existing solutions.Comment: This paper appears in the Proceedings of IEEE Global Communications
Conference (GLOBECOM) 2020. A full version appears in IEEE Transactions on
Wireless Communications. arXiv:2004.1253
Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach
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 (WoLFPHC)
learning approach is proposed to jointly optimize the anti-jamming power
allocation and reflecting beamforming strategy, where WoLFPHC 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
Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems
Reconfigurable intelligent surfaces (RISs) can potentially combat jamming
attacks by diffusing jamming signals. This paper jointly optimizes user
selection, channel allocation, modulation-coding, and RIS configuration in a
multiuser OFDMA system under a jamming attack. This problem is non-trivial and
has never been addressed, because of its mixed-integer programming nature and
difficulties in acquiring channel state information (CSI) involving the RIS and
jammer. We propose a new deep reinforcement learning (DRL)-based approach,
which learns only through changes in the received data rates of the users to
reject the jamming signals and maximize the sum rate of the system. The key
idea is that we decouple the discrete selection of users, channels, and
modulation-coding from the continuous RIS configuration, hence facilitating the
RIS configuration with the latest twin delayed deep deterministic policy
gradient (TD3) model. Another important aspect is that we show a
winner-takes-all strategy is almost surely optimal for selecting the users,
channels, and modulation-coding, given a learned RIS configuration. Simulations
show that the new approach converges fast to fulfill the benefit of the RIS,
due to its substantially small state and action spaces. Without the need of the
CSI, the approach is promising and offers practical value.Comment: accepted by IEEE TCOM in Jan. 202
Outage Constrained Robust BeamformingOptimization for Multiuser IRS-AssistedAnti-Jamming Communications With Incomplete Information
Malicious jamming attacks have been regarded asa serious threat to Internet of Things (IoT) networks, which cansignificantly degrade the quality of service (QoS) of users. Thispaper utilizes an intelligent reflecting surface (IRS) to enhanceanti-jamming performance due to its capability in reconfiguringthe wireless propagation environment via dynamicly adjustingeach IRS reflecting elements. To enhance the communicationperformance against jamming attacks, a robust beamformingoptimization problem is formulated in a multiuser IRS-assistedanti-jamming communications scenario with or without imperfectjammer’s channel state information (CSI). In addition, we furtherconsider the fact that the jammer’s transmit beamforming cannot be known at BS. Specifically, with no knowledge of jammerstransmit beamforming, the total transmit power minimizationproblems are formulated subject to the outage probability re-quirements of legitimate users with the jammer’s statistical CSI,and signal-to-interference-plus-noise ratio (SINR) requirementsof legitimate users without the jammer’s CSI, respectively.By applying the Decomposition-based large deviation inequal-ity (DBLDI), Bernstein-type inequality (BTI), Cauchy-Schwarzinequality, and penalty non-smooth optimization method, weefficiently solve the initial intractable and non-convex problems.Numerical simulations demonstrate that the proposed anti-jamming approaches achieve superior anti-jamming performanceand lower power-consumption compared to the non-IRS schemeand reveal the impact of key parameters on the achievable systemperformance