E. Jayabalan^*, R. Pugazendi

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3757-3784, 2023, DOI:10.32604/iasc.2023.031200

Abstract In this paper, a communication model in cognitive radios is developed and uses machine learning to learn the dynamics of jamming attacks in cognitive radios. It is designed further to make their transmission decision that automatically adapts to the transmission dynamics to mitigate the launched jamming attacks. The generative adversarial learning neural network (GALNN) or generative dynamic neural network (GDNN) automatically learns with the synthesized training data (training) with a generator and discriminator type neural networks that encompass minimax game theory. The elimination of the jamming attack is carried out with the assistance of the defense strategies and with an… More >

T. Jagadesh^1,2, B. Sheela Rani^3,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 449-463, 2023, DOI:10.32604/iasc.2023.026868

Abstract Interference is a key factor in radar return misdetection. Strong interference might make it difficult to detect the signal or targets. When interference occurs in the sidelobes of the antenna pattern, Sidelobe Cancellation (SLC) and Sidelobe Blanking are two unique solutions to solve this problem (SLB). Aside from this approach, the probability of false alert and likelihood of detection are the most essential parameters in radar. The chance of a false alarm for any radar system should be minimal, and as a result, the probability of detection should be high. There are several interference cancellation strategies in the literature that… More >