UC San Diego

Most modern communication networks suffer from both intentional and unintentional interference. In this dissertation, we investigate three separate issues in modern wireless communication networks: sensing disruption attacks on cognitive radio networks (CRNs), sharing disruption attacks on cognitive radio non-orthogonal multiple access (CR-NOMA), and error analysis in millimeter-wave (mmWave) communication under unintentional interference environments.In the first problem, we propose a different approach for sensing disruption attacks in CRNs. We examine the optimal strategy for an intelligent adversary who aims to manipulate busy bands so that they appear to be free. This approach involves contaminating noise power measurements, as demonstrated through a two-step sensing scheme that combines energy detection with noise power estimation by secondary users. We demonstrate that the optimal strategies for sensing link disruptions include equal-power and partial-band flipping, from deriving the maximum average number of missed detections under specific power constraints of the adversary. Secondly, we examine the vulnerabilities of spectrum sharing in a CR-NOMA network, proposing a new type of attack termed sharing disruption. This attack disrupts the channel estimation phase, leading to a denial-of-service (DoS) for secondary users. We derive the optimal power allocation to maximize disruption, calculating the maximum average number of DoS bands under specific adversary power constraints. Additionally, we compare optimal power allocation with uniform power allocation. Lastly, we investigate the error performance of mmWave bands in the presence of unintentional interference. This analysis is motivated by the anticipated increase in number of users in near future. We examine M-ary quadrature amplitude modulation (M-QAM) across Nakagami-m channels, taking into account the impacts of directional antennas and blockage. We derive the average probability of error with employing a stochastic geometry framework that provide different insights for mmWave networks, particularly in device-to-device (D2D) communications.