Proactive eavesdropping via cognitive jamming in fading channels
By Jie Xu, Lingjie Duan, Rui Zhang. 2015
144 citations recorded by the source corpus.
There is a growing need for government agencies to monitor suspicious communication links to prevent crimes and terror attacks. In this paper, we study a legitimate surveillance scenario where a legitimate monitor aims to intercept the suspicious communication between a transmitter and a receiver over fading channels. The legitimate monitor can eavesdrop (decode) the information of the suspicious link only when its achievable data rate is no smaller than that at the suspicious receiver. In practice, the legitimate eavesdropping is challenging, especially when the legitimate monitor is far from the suspicious transmitter. To overcome this issue, we propose a new approach, namely proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver and changes the suspicious communication (e.g., to a smaller data rate) in order to overhear easily. In particular, we consider delay-sensitive and delay-insensitive applications for the suspicious transmission, under which the legitimate monitor maximizes the eavesdropping non-outage probability and the relative eavesdropping rate, respectively, by optimizing its jamming power allocation over different fading states subject to an average power constraint. We present efficient algorithms for optimally solving the formulated problems. Numerical results show that thanks to the cognitive jamming, the proposed proactive eavesdropping scheme greatly outperforms the conventional passive eavesdropping without jamming.
Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Computer Networks and Communications
In Electrical and Electronic Engineering
Citations in the corpus
- Deep Learning for Distributed Optimization: Applications to Wireless Resource Management (2019, background): “Special cases of (P) have been widely studied in various network configurations such as multi-antenna multi-user systems [23], CR networks [24], wireless power transfer communications [26], and proactive eavesdropping applications [27].”
- Physical Layer Security for Next Generation Wireless Networks: Theories, Technologies, and Challenges (2017, background): “Recently, [169] and [170] discussed the issue in a different perspective such that the proactive eavesdroppers can be seen as legitimate monitors.”