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Efficient jamming attack against MIMO transceiver

Published: 01 July 2022 Publication History

Abstract

This work addresses the issue of point-to-point intelligent jamming against MIMO architecture-based systems. The receiver uses an optimal Maximum Likelihood (ML) detector to extract the data stream from the received signal. Analyzing the receiver performance in the presence of a jamming signal, optimal attack strategies based on the pairwise error probability parameter as an objective function are designed, enabling the achievement of maximum degradation of the service quality. To perform the worst-case jamming of a legitimate transceiver, we assume that the jammer can eavesdrop on the Channel State Information (CSI). Depending on the jamming power budget constraint and the potential of jamming systems to estimate the CSI, different scenarios are envisaged, including imperfect CSI estimation cases. By minimizing the trace of Positive Semi-Definite (PSD) matrix products, the optimal jamming approaches that ensure maximum degradation of the legitimate link are designed. The disruption efficiency of the proposed approaches is comparable to the employment of the brute-force attack, but with a higher power level that is proportional to the number of jamming antennas in the case of partial knowledge of CSI, and inversely to the statistical expectation of the square root of the Wishart matrix's eigenvalues in the case of complete knowledge of CSI. To justify the relevance of the suggested policy, extensive simulations of the effect of these interventions are presented.

References

[1]
A.-S. Bana, E. De Carvalho, B. Soret, T. Abrao, J.C. Marinello, E.G. Larsson, P. Popovski, Massive MIMO for Internet of things (IoT) connectivity, Phys. Commun. 37 (2019),.
[2]
K.A. Alnajjar, M. El-Tarhuni, A CV-BLAST spread spectrum massive MIMO NOMA scheme for 5G systems with channel imperfections, Phys. Commun. 35 (2019),.
[3]
A. Masood, D. Scazzoli, N. Sharma, Y. Le Moullec, R. Ahmad, L. Reggiani, M. Magarini, M.M. Alam, Surveying pervasive public safety communication technologies in the context of terrorist attacks, Phys. Commun. 41 (2020),.
[4]
Z. Wang, Z. Zhao, C. Ren, Z. Nie, Adaptive detection of distributed targets in noise and interference which is partially related with targets, Digit. Signal Process. 103 (2020),.
[5]
S. Bhunia, S. Sengupta, F. Vázquez-Abad, Cr-honeynet: a learning & decoy based sustenance mechanism against jamming attack in crn, in: 2014 IEEE Military Communications Conference, 2014, pp. 1173–1180,.
[6]
H. Li, J. Luo, C. Liu, Selfish bandit-based cognitive anti-jamming strategy for aeronautic swarm network in presence of multiple jammer, IEEE Access 7 (2019) 30234–30243,.
[7]
M. Zhang, C. Liao, N. Liu, K. Xu, J. Liu, W. Xie, N. Sha, L. Chen, D. Zhang, Angle-domain secure transmission in air–terrestrial mMIMO system with single antenna ground eavesdropper, Phys. Commun. 36 (2019),.
[8]
Y. Fang, S. Zhu, H. Wang, C. Zeng, DOA estimation via ULA with mutual coupling in the presence of non-uniform noise, Digit. Signal Process. 97 (2020),.
[9]
A. Ruegamer, D. Kowalewski, et al., Jamming and spoofing of GNSS signals–an underestimated risk?!, in: Proc. Wisdom Ages Challenges Modern World, vol. 3, 2015, pp. 17–21.
[10]
M. Lichtman, J.D. Poston, S. Amuru, C. Shahriar, T.C. Clancy, R.M. Buehrer, J.H. Reed, A communications jamming taxonomy, IEEE Secur. Priv. 14 (1) (2016) 47–54,.
[11]
S. Bayram, N.D. Vanli, B. Dulek, I. Sezer, S. Gezici, Optimum power allocation for average power constrained jammers in the presence of non-Gaussian noise, IEEE Commun. Lett. 16 (8) (2012) 1153–1156,.
[12]
J. Wang, J. Liu, N. Kato, Networking and communications in autonomous driving: a survey, IEEE Commun. Surv. Tutor. 21 (2) (2018) 1243–1274,.
[13]
S. Amuru, R.M. Buehrer, Optimal jamming against digital modulation, IEEE Trans. Inf. Forensics Secur. 10 (10) (2015) 2212–2224,.
[14]
S. Amuru, C. Tekin, M. van der Schaar, R.M. Buehrer, Jamming bandits—a novel learning method for optimal jamming, IEEE Trans. Wirel. Commun. 15 (4) (2015) 2792–2808,.
[15]
A. Mukherjee, A.L. Swindlehurst, Jamming games in the MIMO wiretap channel with an active eavesdropper, IEEE Trans. Signal Process. 61 (1) (2012) 82–91,.
[16]
D. Feng, L. Xu, X. Pan, X. Wang, Jamming wideband radar using interrupted-sampling repeater, IEEE Trans. Aerosp. Electron. Syst. 53 (3) (2017) 1341–1354,.
[17]
J. Gao, S.A. Vorobyov, H. Jiang, H.V. Poor, Worst-case jamming on MIMO Gaussian channels, IEEE Trans. Signal Process. 63 (21) (2015) 5821–5836,.
[18]
Q. Liu, M. Li, X. Kong, N. Zhao, Disrupting MIMO communications with optimal jamming signal design, IEEE Trans. Wirel. Commun. 14 (10) (2015) 5313–5325,.
[19]
J. Vinogradova, E. Björnson, E.G. Larsson, Jamming massive MIMO using massive MIMO: asymptotic separability results, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 3454–3458,.
[20]
M.A.M. Sadr, M. Ahmadian-Attari, R. Amiri, V.V. Sabegh, Worst-case jamming attack and optimum defense strategy in cooperative relay networks, IEEE Control Syst. Lett. 3 (1) (2018) 7–12,.
[21]
G. Zhang, J. Xu, Q. Wu, M. Cui, X. Li, F. Lin, Wireless powered cooperative jamming for secure OFDM system, IEEE Trans. Veh. Technol. 67 (2) (2017) 1331–1346,.
[22]
X. Chen, L. Guo, X. Li, C. Dong, J. Lin, C.C. Cavalcante, Full-duplex wireless-powered jammer aided secure communication for cognitive radio networks, Phys. Commun. 31 (2018) 103–112,.
[23]
Q. Gao, Y. Huo, L. Ma, X. Xing, X. Cheng, T. Jing, H. Liu, Joint design of jammer selection and beamforming for securing MIMO cooperative cognitive radio networks, IET Commun. 11 (8) (2017) 1264–1274,.
[24]
C. Wang, E.K. Au, R.D. Murch, W.H. Mow, R.S. Cheng, V. Lau, On the performance of the MIMO zero-forcing receiver in the presence of channel estimation error, IEEE Trans. Wirel. Commun. 6 (3) (2007) 805–810,.
[25]
H. Lim, D. Yoon, On the distribution of SINR for MMSE MIMO systems, IEEE Trans. Commun. 67 (6) (2019) 4035–4046,.
[26]
A. Bhargave, R.J. de Figueiredo, T. Eltoft, A detection algorithm for the V-BLAST system, in: GLOBECOM'01, IEEE Global Telecommunications Conference (Cat. No. 01CH37270), vol. 1, 2001, pp. 494–498,.
[27]
S.J. Grant, J.K. Cavers, Performance enhancement through joint detection of cochannel signals using diversity arrays, IEEE Trans. Commun. 46 (8) (1998) 1038–1049,.
[28]
Ş.B. Akdemir, Ç. Candan, Maximum-likelihood direction of arrival estimation under intermittent jamming, Digit. Signal Process. 113 (2021),.
[29]
H. Sung, K. Bok, J.W. Kang, A simplified maximum likelihood detection scheme for MIMO systems, in: 2003 IEEE 58th Vehicular Technology Conference, VTC 2003-Fall (IEEE Cat. No. 03CH37484), vol. 1, 2003, pp. 419–423.
[30]
L. Bai, J. Choi, Low Complexity MIMO Detection, Springer Science & Business Media, 2012.
[31]
J. Choi, J. Mo, R.W. Heath, Near maximum-likelihood detector and channel estimator for uplink multiuser massive MIMO systems with one-bit ADCs, IEEE Trans. Commun. 64 (5) (2016) 2005–2018,.
[32]
A. Datta, V. Bhatia, A near maximum likelihood performance modified firefly algorithm for large MIMO detection, Swarm Evol. Comput. 44 (2019) 828–839,.
[33]
H. Hijazi, A. Haroun, M. Saad, A.C. Al Ghouwayel, A. Dhayni, Near-optimal performance with low-complexity ML-based detector for MIMO spatial multiplexing, IEEE Commun. Lett. 25 (1) (2020) 122–126,.
[34]
J. Minango, A.C. Flores, Low-complexity MMSE detector based on refinement Jacobi method for massive MIMO uplink, Phys. Commun. 26 (2018) 128–133,.
[35]
K. Kuchi, A.B. Ayyar, Performance analysis of ML detection in MIMO systems with co-channel interference, IEEE Commun. Lett. 15 (8) (2011) 786–788,.
[36]
M. Chiani, D. Dardari, M.K. Simon, New exponential bounds and approximations for the computation of error probability in fading channels, IEEE Trans. Wirel. Commun. 2 (4) (2003) 840–845,.
[37]
X. Zhou, B. Maham, A. Hjorungnes, Pilot contamination for active eavesdropping, IEEE Trans. Wirel. Commun. 11 (3) (2012) 903–907,.
[38]
C. Buiquang, Z. Ye, Constrained ALS-based tensor blind receivers for multi-user MIMO systems, Digit. Signal Process. 84 (2019) 69–79,.
[39]
A.L. de Almeida, G. Favier, J.C.M. Mota, Parafac-based unified tensor modeling for wireless communication systems with application to blind multiuser equalization, Signal Process. 87 (2) (2007) 337–351.
[40]
N.D. Sidiropoulos, G.B. Giannakis, R. Bro, Blind PARAFAC receivers for DS-CDMA systems, IEEE Trans. Signal Process. 48 (3) (2000) 810–823,.
[41]
P. Savazzi, A. Vizziello, D. Benfatto, C. Salvaneschi, Channel parameter acquisition in MIMO LOS systems for high capacity microwave links, Phys. Commun. 34 (2019) 196–202,.
[42]
F. Dufossé, B. Uçar, Notes on Birkhoff–Von Neumann decomposition of doubly stochastic matrices, Linear Algebra Appl. 497 (2016) 108–115,.
[43]
V. Anantharam, A. Lapidoth, E. Biglieri, B. McEliece, G. Caire, N. Merhav, R. Cheng, D. Neuhoff, K. Chen, A. Orlitsky, et al., Foundations and TrendsTM in Communications and Information Theory, 1st edition, Citeseer, 2004.
[44]
T.L. Marzetta, BLAST Training: Estimating Channel Characteristics for High Capacity Space-Time Wireless, Citeseer, 1999.
[45]
S. Loyka, V. Kostina, F. Gagnon, Error rates of the maximum-likelihood detector for arbitrary constellations: convex/concave behavior and applications, IEEE Trans. Inf. Theory 56 (4) (2010) 1948–1960,.
[46]
R. McEliece, W. Stark, An information theoretic study of communication in the presence of jamming, in: ICC'81; International Conference on Communications, vol. 3, 1981.

Cited By

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  • (2024)Anti-jamming power allocation scheme for a multi-static MIMO radar network based on mutual informationDigital Signal Processing10.1016/j.dsp.2023.104335145:COnline publication date: 12-Apr-2024

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Published In

cover image Digital Signal Processing
Digital Signal Processing  Volume 127, Issue C
Jul 2022
671 pages

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Academic Press, Inc.

United States

Publication History

Published: 01 July 2022

Author Tags

  1. MIMO
  2. Maximum likelihood detector
  3. Jamming signal
  4. Channel state information

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  • (2024)Anti-jamming power allocation scheme for a multi-static MIMO radar network based on mutual informationDigital Signal Processing10.1016/j.dsp.2023.104335145:COnline publication date: 12-Apr-2024

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