Attacking Massive MIMO Cognitive Radio Networks by Optimized Jamming
Journal article, 2021

Massive multiple-input multiple-output (MaMIMO) and cognitive radio networks (CRNs) are two promising technologies for improving spectral efficiency of next-generation wireless communication networks. In this paper, we investigate the problem of physical layer security in the networks that jointly use both technologies, named MaMIMO-CRN. Specifically, to investigate the vulnerability of this network, we design an optimized attacking scenario to MaMIMO-CRNs by a jammer. For having the most adversary effect on the uplink transmission of the legitimate MaMIMO-CRN, we propose an efficient method for power allocation of the jammer. The legitimate network consists of a training and a data transmission phase, and both of these phases are attacked by the jammer using an optimized power split between them. The resulting power allocation problem is non-convex. We thus propose three different efficient methods for solving this problem, and we show that under some assumptions, a closed-form solution can also be obtained. Our results show the vulnerability of the MaMIMO-CRN to an optimized jammer. It is also shown that increasing the number of antennas at the legitimate network does not improve the security of the network.

Physical layer security

power allocation

cognitive radio network

Antennas

jamming

Massive MIMO

Channel estimation

optimization

Training

Uplink

Jamming

spectral efficiency

Resource management

physical layer security

Author

S. Fatemeh Zamanian

Iran University of Science and Technology

Mohammad Hossein Kahaei

Iran University of Science and Technology

S. Mohammad Razavizadeh

Iran University of Science and Technology

Tommy Svensson

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

IEEE Open Journal of the Communications Society

2644125X (eISSN)

Vol. 2 2219-2231

Subject Categories

Telecommunications

Communication Systems

Signal Processing

DOI

10.1109/OJCOMS.2021.3114382

More information

Latest update

1/3/2024 9