Performance Analysis of Reactive Symbol-Level Jamming Techniques

The reactive symbol-level jamming (SLJ) technique is a simple but practical technique to disable or disrupt malicious communication links. After sensing the transmission and detecting the digital modulation scheme of the malicious transmitter, a communication node with the reactive SLJ technique, called a reactive jammer, generates random digital modulation symbols and then sends them to the malicious receiver. In this correspondence, we mathematically analyze both uncoded and coded bit error rate (BER) at the malicious receiver under not only reactive SLJ, but also Gaussian SLJ techniques. In particular, we consider the partial jamming scenario, in which a portion of symbols in a data frame are affected by the SLJ, which is a practical scenario under the reactive jamming techniques. We also consider the effect of imperfect power control and channel estimation error of the reactive jammer on the BER performance at the malicious receiver.

[1]  Ali Abdi,et al.  Survey of automatic modulation classification techniques: classical approaches and new trends , 2007, IET Commun..

[2]  Roger Piqueras Jover,et al.  LTE/LTE-A jamming, spoofing, and sniffing: threat assessment and mitigation , 2016, IEEE Communications Magazine.

[3]  Jie Xu,et al.  Fundamental Rate Limits of Physical Layer Spoofing , 2017, IEEE Wireless Communications Letters.

[4]  Lajos Hanzo,et al.  A Survey on Wireless Security: Technical Challenges, Recent Advances, and Future Trends , 2015, Proceedings of the IEEE.

[5]  Andrew J. Viterbi,et al.  Convolutional Codes and Their Performance in Communication Systems , 1971 .

[6]  R. Michael Buehrer,et al.  Optimal Jamming Against Digital Modulation , 2015, IEEE Transactions on Information Forensics and Security.

[7]  Jie Xu,et al.  Surveillance and Intervention of Infrastructure-Free Mobile Communications: A New Wireless Security Paradigm , 2016, IEEE Wireless Communications.

[8]  Yu-Dong Yao,et al.  Modulation Classification Based on Signal Constellation Diagrams and Deep Learning , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Jean Conan The Weight Spectra of Some Short Low-Rate Convolutional Codes , 1984, IEEE Trans. Commun..

[10]  Xiqi Gao,et al.  A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead , 2018, IEEE Journal on Selected Areas in Communications.

[11]  R. Michael Buehrer,et al.  A communications jamming taxonomy , 2016, IEEE Security & Privacy.

[12]  Marwan Krunz,et al.  Secrecy beyond encryption: obfuscating transmission signatures in wireless communications , 2015, IEEE Communications Magazine.

[13]  Jie Xu,et al.  Transmit Optimization for Symbol-Level Spoofing , 2018, IEEE Transactions on Wireless Communications.