Title: Deep learning based PSA detection model in multi-user M-MIMO networks

Authors: V.M. Manju; R.S. Ganesh

Addresses: Department of Electronics and Communication Engineering, Noorul Islam centre for Higher Education, Thuckalay, Kumaracoil, 629180, Tamil Nadu, India ' Department of Electronics and Communication Engineering, PET Engineering College, Vallioor, Tamil Nadu, India

Abstract: It is commonly known that MASSIVE MIMO (M-MIMO) is a key component for the forthcoming wireless networks. Base stations (BSs) in M-MIMO networks are fitted with an enormous number of antennas to provide several advantages over conventional MIMO, including easier power control, improved spectrum efficiency, and increased efficiency of energy. Since the estimated channel state information (CSI) might be contaminated by the eavesdrop interaction; M-MIMO systems are susceptible to pilot spoofing attacks (PSAs), which result in significant information leakage in the downstream transmission. To protect genuine communications, this work introduces a new PSA detection model in multiuser M-MIMO (MU M-MIMO). Initially, signal transmission takes place, and then the large-scale fading factors are estimated. Further, PSA detection is done using a deep neural network (DNN) framework. Finally, the optimal channel estimation is done using a Self Customised Black Widow Optimisation Algorithm (SC-BWO). Moreover, analysis is performed on error probability, bit error rate (BER), and so on.

Keywords: MASSIVE MIMO; M-MIMO; PSAs; pilot spoofing attacks; multiuser; DNN; deep neural network; SC-BWO algorithm.

DOI: 10.1504/IJAACS.2025.149804

International Journal of Autonomous and Adaptive Communications Systems, 2025 Vol.18 No.5, pp.381 - 402

Received: 18 Oct 2023
Accepted: 24 Feb 2024
Published online: 13 Nov 2025 *

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