Journal ArticleDOI
Jamming Resilient Communication Using MIMO Interference Cancellation
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TLDR
A MIMO-based anti-jamming scheme that exploits MIMo interference cancellation and transmit precoding technologies to turn a jammed non-connectivity scenario into an operational network and is implemented using software-defined radios.Abstract:
Jamming attack is a serious threat to the wireless communications. Reactive jamming maximizes the attack efficiency by jamming only when the targets are communicating, which can be readily implemented using software-defined radios. In this paper, we explore the use of the multi-input multi-output (MIMO) technology to achieve jamming resilient orthogonal frequency-division multiplexing (OFDM) communication. In particular, MIMO interference cancellation treats jamming signals as noise and strategically cancels them out, while transmit precoding adjusts the signal directions to optimize the decoding performance. We first investigate the reactive jamming strategies and their impacts on the MIMO-OFDM receivers. We then present a MIMO-based anti-jamming scheme that exploits MIMO interference cancellation and transmit precoding technologies to turn a jammed non-connectivity scenario into an operational network. We implement our jamming resilient communication scheme using software-defined radios. Our testbed evaluation shows the destructive power of reactive jamming attack, and also validates the efficacy and efficiency of our defense mechanisms in the presence of numerous types of reactive jammers with different jamming signal powers.read more
Citations
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Journal ArticleDOI
Reinforcement Learning-Based NOMA Power Allocation in the Presence of Smart Jamming
TL;DR: Simulation results show that the proposed reinforcement learning-based power control scheme for the downlink NOMA transmission can significantly increase the sum data rates of users, and thus, the utilities compared with the standard Q-learning-based strategy.
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Jamming Strategies for Physical Layer Security
TL;DR: This article provides a comprehensive overview on jamming strategies in the physical layer for securing wireless communications and classification from three different perspectives and explains the major related designs in various scenarios.
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Two-Dimensional Antijamming Mobile Communication Based on Reinforcement Learning
TL;DR: A hotbooting deep Q-network based 2-D mobile communication scheme is proposed that exploits experiences in similar scenarios to reduce the exploration time at the beginning of the game, and applies deep convolutional neural network and macro-action techniques to accelerate learning in dynamic situations.
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Deep Reinforcement Learning-Enabled Secure Visible Light Communication Against Eavesdropping
TL;DR: Simulation results show that the proposed learning-based VLC beamforming control schemes can significantly decrease the bit error rate of the legitimate receiver and increase the secrecy rate and utility of the anti-eavesdropping MISO VLC system, compared with the benchmark strategy.
Journal ArticleDOI
Jamming-Resistant Receivers for the Massive MIMO Uplink
TL;DR: Interestingly, the proposed jamming-resistant receiver scheme works particularly well under strong jamming attacks, since the improved estimate of the jamming channel outweighs the extra jamming power.
References
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David Tse,Pramod Viswanath +1 more
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Proceedings ArticleDOI
The feasibility of launching and detecting jamming attacks in wireless networks
TL;DR: This paper proposes four different jamming attack models that can be used by an adversary to disable the operation of a wireless network, and evaluates their effectiveness in terms of how each method affects the ability of a Wireless node to send and receive packets.
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George C. Clark,J. Bibb Cain +1 more
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