Signal Detection in Cognitive Radio Networks over AWGN and Fading Channels

Patil, Vilaskumar; Ujjinimatad, Rohitha; Patil, Siddarama R.

doi:10.1007/s10776-017-0376-x

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…Jamming occurs when the accumulated signal energy in the frequency band exceeds threshold which is given based on the Neyman‐Pearson detection criterion with constant false alarm rate. For simplicity, the probability of detection for radiometer can be expressed as 23

{\mathbf{\Pr}}_d=1-\exp \left(-\frac{P_T{g}_3}{\sigma^2}\right)\left(\exp \left(\frac{\gamma_{\mathrm{th}}}{\sigma^2}\right)-1\right),

where

{\gamma}_{th}

denotes SINR threshold,

{\sigma}^2

denotes the received noise power, and

{P}_T

is defined as the transmit power of caller radio.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Joint power and hopping rate adaption against follower jammer based on deep reinforcement learning

Wang

Zhang

2022

Trans Emerging Tel Tech

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As an effective anti-jamming approach, frequency-hopping (FH) technology has been widely applied to tactical communication system, providing reliable communication guarantee and improving resilience against conventional interference under strong confrontation environment. Key challenges for tactical wireless communication network face are the smart follower jammer with responsive spectrum reconnaissance and intelligent decision-making capabilities. In response, this article investigates a deep reinforcement learning based anti-jamming scheme, with the aim of maximizing the system throughput. The interactions between a radio transmitter and a smart follower jammer are formulated as a hierarchical anti-jamming dynamic game model, in which the radio terminal decides transmission power and hopping rate according to the state feedback information, and the jammer chooses spectrum scanning rate accordingly to minimizing the rewards of the FH communication system. We prove that there is a Nash equilibrium (NE) strategy for static and dynamic environment in the game. A double deep Q-network with prioritized experience reply (PDDQN) based anti-jamming scheme is proposed to approximating the optimal power control and hopping strategy without being aware of the environment and jamming parameters. Finally, simulation results demonstrate that the proposed algorithm efficiently provide better throughput and jamming resistance. INTRODUCTIONDue to military communication network's heavy reliance on robust links and perceptivity to forms of hostile electronic attack, wireless communication system must be equipped with robust interference suppression capabilities in contested environments. As an effective anti-jamming method to improve link reliability and anti-jamming characteristics, broadband high-speed frequency-hopping (FH) technology has been applied extensively in the field of military tactical communication to weaken the effects of various types of intentional jamming. Variable hopping rate and power adaption are commonly used techniques to mitigate interference separately. 1,2 However, conventional method cannot adapt to the dynamic environment especially in the tactical command and control (C2) network with high mobility user node. Our target in this article is to investigate the effectiveness of a jointly optimized adaption approach which has flexible control over the signal strength and hopping rate.

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{\mathbf{\Pr}}_d=1-\exp \left(-\frac{P_T{g}_3}{\sigma^2}\right)\left(\exp \left(\frac{\gamma_{\mathrm{th}}}{\sigma^2}\right)-1\right),

where

{\gamma}_{th}

denotes SINR threshold,

{\sigma}^2

denotes the received noise power, and

{P}_T

is defined as the transmit power of caller radio.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Joint power and hopping rate adaption against follower jammer based on deep reinforcement learning

Wang

Zhang

2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…It is preferable for widerband spectrum sensing. The functional block diagram of ED is given in Figure 2 which consists of four main parts [9]. The input signal is first sent through the bandpass filter that is then sent to the squaring device and integrator.The output from the integrator is the signal which is received at the 'T' time interval.…”

Section: System Modelmentioning

confidence: 99%

Spectrum Sensing Using Energy Detector in Rayleigh Fading Channel

et al. 2020

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Cognitive Radio, which is an adaptive and intelligent radio has an important feature called spectrum sensing. It monitors the presence or absence of the authorized users in a fixed or licensed spectrum. There is a rise in the use of wireless communications in recent times, which has led to the underutilization of the available bandwidth. This problem can be solved to some extent by using cognitive radio technology. Cognitive radio gives an opportunity for unauthorized users to use the licensed spectrum, resourcefully, when not in use. Using spectrum sensing energy detection methods, the existence of authorized users, in various fading channels can be done. The operating characteristics of the receiver can be analyzed using parameters of performance like signal to noise ratio, probability of false-alarm, probability of detection. In this paper we assess Rayleigh fading channel and compare with AWGN (non- fading) channel. Theoretical and simulated results are compared, and observations are being made.

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