Wireless legitimate surveillance via jamming in MISO cognitive radio networks

Abstract

In this paper, we study proactive eavesdropping over multiple-input signal-output (MISO) cognitive radio networks, where a legitimate monitor tries to eavesdrop on the suspicious secondary link via jamming. Different from the existing work on jamming-aided proactive eavesdropping, our jamming signals here can change the suspicious secondary transmitter’s beamforming strategy in favor of the legitimate monitor’s eavesdropping. To be specific, we are interested in maximizing the achievable eavesdropping rate by optimizing the jamming beamforming vector, subject to the interference temperature constraint at the primary receiver and the maximum transmit power constraint at the monitor. The original jamming beamforming problem is non-convex and thus difficult to solve optimally. To gain more useful insights, we first consider the case where the suspicious secondary transmitter has a single-antenna, and the corresponding optimal jamming beamforming solution is derived in closed-form. We then extend our study to the general case where the secondary transmitter has multiple antennas. For this case, we propose an efficient algorithm to deal with the corresponding jamming beamforming design problem. The proposed solution reveals that the optimal beamforming vector balances the interference to the suspicious secondary receiver and the primary receiver. Finally, numerical results show that the proposed optimal scheme significantly improves the eavesdropping rate as compared to the benchmark schemes.