Cognitive Radio Network: Security and Reliability trade-off - Status, Challenges, and Future trend

Jasim, Doaa Kareem; Sadkhan, Sattar B.

doi:10.1109/bicits51482.2021.9509929

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…Under this interpretation the reward for a jammer agent (J) will be (6) 𝑅 w2 = −1 × 𝑅 w1 . Using above equations, we get (7) 𝑅 w = { 1 if SNR R U ≥ SNR cutoff , −1 otherwise, where cutoff is demodulation threshold, when the SNR at receiver (SNR R U ) is lower than this threshold then the data transmission fails, therefore the agent for jamming process will get positive reward while agent for anti-jamming process will get negative reward.…”

Section: Environmentmentioning

confidence: 99%

“…A variety of work has been carried out on the topic of anti-jamming and jamming schemes as discussed in [6]. The traditional approaches in the context of anti-jamming communication, mostly rely on pure signal processing methods, which presents significant performance limitations [7,8]. Although in the past recent years jamming issues in CRNs are being addressed by machine learning techniques, game theory method, and Markov decision processes [9,10].…”

Section: Introductionmentioning

confidence: 99%

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A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Sudha

2022

Cybernetics and Information Technologies

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Modern networking systems can benefit from Cognitive Radio (CR) because it mitigates spectrum scarcity. CR is prone to jamming attacks due to shared communication medium that results in a drop of spectrum usage. Existing solutions to jamming attacks are frequently based on Q-learning and deep Q-learning networks. Such solutions have a reputation for slow convergence and learning, particularly when states and action spaces are continuous. This paper introduces a unique reinforcement learning driven anti-jamming scheme that uses adversarial learning mechanism to counter hostile jammers. A mathematical model is employed in the formulation of jamming and anti-jamming strategies based on deep deterministic policy gradients to improve their policies against each other. An open-AI gym-oriented customized environment is used to evaluate proposed solution concerning power-factor and signal-to-noise-ratio. The simulation outcome shows that the proposed anti-jamming solution allows the transmitter to learn more about the jammer and devise the optimal countermeasures than conventional algorithms.

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Section: Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Sudha

2022

Cybernetics and Information Technologies

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“…However, the above techniques still could not solve the spectrum limitation of IoT. Faced with the spectrum limitation, cognitive radio (CR) is widely studied, where the spectrum sensing technique is applied to find the idle spectrum [22]- [25]. In the CR networks, the secondary users could use the authorized spectrum of primary users (PUs).…”

Section: Introductionmentioning

confidence: 99%

Utility-Based Cooperative Resource Sharing in Symbiotic-Radio-Aided Internet of Things Networks

Liang

Wen

et al. 2023

IEEE Internet Things J.

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Symbiotic radio (SR) is a key technique to solve the energy shortage and spectrum limitation of the future Internet of Things (IoT). In the SR-aided IoT networks supporting energy harvesting (EH), we study the cooperation schemes and offloading strategy between the primary users (PUs), IoT devices and the base station (BS) for reasonably allocating the spectrum, power and time resources. Considering the monetary transactions between the PUs and IoT devices, two cooperation schemes, namely the "Preferential Scenario" and the "No-Preferential Scenario", are proposed. In the "Preferential Scenario", based on the final strategy, the IoT devices use the purchased spectrum and power to offload their own tasks to the BS after assisting the cooperative PUs to offload during a certain time slot. Due to the assistance of IoT devices for the PUs, IoT devices enjoy a discount when paying for the purchased spectrum and power. In the "No-Preferential Scenario", the IoT devices and the cooperative PUs offload tasks to the BS together in a certain time slot according to the offloading strategy. The spectrum and power used by the IoT devices are purchased at the original price without a discount. For each scenario, we study the utility maximization problem of the PUs, where the utility of PUs includes the transmission rates and income. The utility based resource sharing algorithm is proposed to obtain an approximately optimal resource allocation scheme. Our simulation results indicate that the proposed algorithm provides good performances for both scenarios, while each scenario applying the proposed algorithm has its own advantages.

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Efficient Optimized ATSDERP Routing Based DEQRL Spectrum Sharing HPNCS Network Coding Model in Cognitive Radio Networks

Gupta

Joshi

2023

Wireless Pers Commun

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Cognitive Radio Network: Security and Reliability trade-off - Status, Challenges, and Future trend

Cited by 11 publications

References 20 publications

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Utility-Based Cooperative Resource Sharing in Symbiotic-Radio-Aided Internet of Things Networks

Efficient Optimized ATSDERP Routing Based DEQRL Spectrum Sharing HPNCS Network Coding Model in Cognitive Radio Networks

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