Power control with reinforcement learning in cooperative cognitive radio networks against jamming

Xiao, Liang; Li, Yan; Liu, Jinliang; Zhao, Yifeng

doi:10.1007/s11227-015-1420-1

Cited by 70 publications

(40 citation statements)

References 20 publications

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“…The regret of the CUJ algorithm is same as the CNJ algorithm. (1), where the values of T C , T O , and T J are given in (17), (18), and (19), respectively with 1 = 2 = γ exp(1).K . Proof: Channel Ranking Estimation This part is similar to channel ranking estimation of Section 6.…”

Section: Appendix C Analysis Of Cuj Algorithmmentioning

confidence: 99%

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Sawant

Kumar

Hanawal

et al. 2020

IEEE Trans. on Mobile Comput.

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Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability.We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiment. IndexTerms-Cognitive radio network, jamming attack, distributed learning ! PLACE PHOTO HERE Sunnet Sawant received his B.Tech and M.Tech. dual degree in Electrical Engineering from IIT Bombay in 2017. His research interests include communication networks PLACE PHOTO HERE Rohit Kumar received his B.

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Section: Appendix C Analysis Of Cuj Algorithmmentioning

confidence: 99%

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Sawant

Kumar

Hanawal

et al. 2020

IEEE Trans. on Mobile Comput.

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“…For instance, the Colonel Blotto anti-jamming game presented in [14] provides a power allocation strategy to improve the worst-case performance against jamming in cognitive radio networks. The power control Stackelberg game as presented in [15] formulates the interactions among a source node, a relay node and a jammer that choose their transmit power in sequence without interfering with primary users. The transmission Stackelberg game developed in [16] helps build a power allocation strategy to maximize the SINR of signals in wireless networks.…”

Section: Related Workmentioning

confidence: 99%

“…where α is the learning rate that represents the weight of the current Q-function. As a benchmark, we propose a greedy-based 2-D antijamming mobile communication scheme, which update the score of each feasible communication strategy according to Receive the SINR (k) and ψ (k+1) on the feedback channel 15 Obtain u (k) and s (k+1) = SINR (k) , ψ (k+1)…”

Section: Application and Performance Evaluationmentioning

confidence: 99%

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Two-dimensional anti-jamming communication based on deep reinforcement learning

Han

Xiao

Poor

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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175

108

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By using smart radio devices, a jammer can dynamically change its jamming policy based on opposing security mechanisms; it can even induce the mobile device to enter a specific communication mode and then launch the jamming policy accordingly. On the other hand, mobile devices can exploit spread spectrum and user mobility to address both jamming and interference. In this paper, a two-dimensional anti-jamming mobile communication scheme is proposed in which a mobile device leaves a heavily jammed/interfered-with frequency or area. It is shown that, by applying reinforcement learning techniques, a mobile device can achieve an optimal communication policy without the need to know the jamming and interference model and the radio channel model in a dynamic game framework. More specifically, a hotbooting deep Q-network based two-dimensional 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 the learning speed in dynamic situations. Several real-world scenarios are simulated to evaluate the proposed method. These simulation results show that our proposed scheme can improve both the signal-to-interference-plus-noise ratio of the signals and the utility of the mobile devices against cooperative jamming compared with benchmark schemes.

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“…The difference from [28] is that they consider relay nodes which help the source counteract a smart jammer. Furthermore, in [29] reinforcement learning can be applied to determine transmission powers against a jammer in a dynamic environment without knowing the underlying Game model. In [1], authors propose an anti-jamming Bayesian Stackelberg Game with incomplete information.…”

Section: Related Workmentioning

confidence: 99%

Smart Jamming Attacks in Wireless Networks During a Transmission Cycle: Stackelberg Game with Hierarchical Learning Solution

Lmater¹,

Haddad²,

Karouit³

et al. 2018

ijacsa

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Abstract-Due to the broadcast nature of the shared medium, wireless communications become more vulnerable to malicious attacks. In this paper, we tackle the problem of jamming in wireless network when the transmission of the jammer and the transmitter occur with a non-zero cost. We focus on a jammer who keeps track of the re-transmission attempts of the packet until it is dropped. Firstly, we consider a power control problem following a Nash Game model, where all players take action simultaneously. Secondly, we consider a Stackelberg Game model, in which the transmitter is the leader and the jammer is the follower. As the jammer has the ability to sense the transmission power, the transmitter adjusts its transmission power accordingly, knowing that the jammer will do so. We provide the closed-form expressions of the equilibrium strategies where both the transmitter and the jammer have a complete information. Thereafter, we consider a worst case scenario where the transmitter has an incomplete information while the jammer has a complete information. We introduce a Reinforcement Learning method, thus, the transmitter can act autonomously in a dynamic environment without knowing the above Game model. It turns out that despite the jammer ability of sensing the active channel, the transmitter can enhance its efficiency by predicting the jammer reaction according to its own strategy.

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Power control with reinforcement learning in cooperative cognitive radio networks against jamming

Cited by 70 publications

References 20 publications

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers

Two-dimensional anti-jamming communication based on deep reinforcement learning

Smart Jamming Attacks in Wireless Networks During a Transmission Cycle: Stackelberg Game with Hierarchical Learning Solution

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