Integrated sensing and communication (ISAC) is a key enabler for next-generation wireless communication systems to improve spectral efficiency. However, the coexistence of sensing and communication functionalities can cause harmful interference. In this paper, we propose to use a reconfigurable intelligent surface (RIS) in conjunction with ISAC to address this issue. The RIS is composed of a large number of low-cost elements that can adjust the amplitude and phase shift of impinging signals, thus providing a relatively high beamforming gain. To maximize the sum-rate of the communication system, we jointly optimize the beamformer at the base station (BS) and the phase shifts at the RIS, subject to a threshold on the interference power, the unit-norm constraint of the transmit power, and the unit modulus constraint of the RIS phase shifts. To efficiently tackle this NP-hard problem, we first reformulate the problem into a more tractable form using the fractional programming (FP) technique. Then, we exploit the geometrical properties of the constraints and adopt an alternating manifold-based optimization to compute the optimal active beamformer and the RIS phase shifts, respectively. Simulation results demonstrate that the proposed RIS-assisted design significantly reduces the mutual interference and improves the system sum-rate for the communication system.

Automatic modulation classification (AMC) using the Deep Neural Network (DNN) approach outperforms the traditional classification techniques, even in the presence of challenging wireless channel environments. However, the adversarial attacks cause the loss of accuracy for the DNN-based AMC by injecting a well-designed perturbation to the wireless channels. In this paper, we propose a novel generative adversarial network (GAN)-based countermeasure approach to safeguard the DNN-based AMC systems against adversarial attack examples. GAN-based aims to eliminate the adversarial attack examples before feeding to the DNN-based classifier. Specifically, we have shown the resiliency of our proposed defense GAN against the Fast-Gradient Sign method (FGSM) algorithm as one of the most potent kinds of attack algorithms to craft the perturbed signals. The existing defense-GAN has been designed for image classification and does not work in our case where the above-mentioned communication system is considered. Thus, our proposed countermeasure approach deploys GANs with a mixture of generators to overcome the mode collapsing problem in a typical GAN facing radio signal classification problem. Simulation results show the effectiveness of our proposed defense GAN so that it could enhance the accuracy of the DNN-based AMC under adversarial attacks to 81%, approximately.

Next-generation wireless communication systems are believed to share the same spectrum previously allocated to radar applications. The coexisting communication system will cause harmful interference to the radar system. In this paper, we investigate the deployment of the Reconfigurable intelligent surface (RIS) to improve the performance of a Multiple-Input Multiple-Output (MIMO) Radar and Communication Coexis-tence (RCC) system. The RIS consists of a large number of nearly passive, and low-cost elements, which provides passive, and a relatively high beamforming gain by controlling the reflecting elements' reflection coefficients. Moreover, the RIS can eliminate the mutual interference between the radar and communication systems. To improve the sum-rate of the communication system subjected to the radar performance constraints, we design the transmit beamforming and the phase shifts for the RIS elements by using the local search approach. Numerical results verify the effectiveness of the utilization of the RIS.