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IEEE Wireless Communications Letters

Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach

Dongxuan He, Chenxi Liu, Tony Q. S. Quek, Hua Wang

Published Feb 14, 2018• 104 citations
Publisher

Abstract

In this letter, we exploit the potential benefits of machine learning in enhancing physical layer security in multi-input multi-output multi-antenna-eavesdropper wiretap channels. To this end, we focus on the scenario where the source adopts transmit antenna selection (TAS) as the transmission strategy. We assume that the channel state information (CSI) of the legitimate receiver is available to the source, while the CSI of the eavesdropper can be either known or not known at the source. By modeling the problem of TAS as a multiclass classification problem, we propose two machine learning-based schemes, namely, the support vector machine-based scheme and the naive-Bayes-based scheme, to select the optimal antenna that maximizes the secrecy performance of the considered system. Compared to the conventional TAS scheme, we show that our proposed schemes can achieve almost the same secrecy performance with relatively small feedback overhead. The work presented here provides insights into the design of new machine learning-based secure transmission schemes.

DOI
10.1109/lwc.2018.2805902

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In this letter, we exploit the potential benefits of machine learning in enhancing physical layer security in multi-input multi-output multi-antenna-eavesdropper wiretap channels. To this end, we focus on the scenario where the source adopts transmit antenna selection (TAS) as the transmission strategy. We assume that the channel state information (CSI) of the legitimate receiver is available to the source, while the CSI of the eavesdropper can be either known or not known at the source. By modeling the problem of TAS as a multiclass classification problem, we propose two machine learning-based schemes, namely, the support vector machine-based scheme and the naive-Bayes-based scheme, to select the optimal antenna that maximizes the secrecy performance of the considered system. Compared to the conventional TAS scheme, we show that our proposed schemes can achieve almost the same secrecy performance with relatively small feedback overhead. The work presented here provides insights into the design of new machine learning-based secure transmission schemes.
This paper proposes a new transmit antenna selection (TAS) scheme which provides enhanced physical layer security in multiple-input multiple-output (MIMO) wiretap channels. The practical passive eavesdropping scenario we consider is where channel state information (CSI) from the eavesdropper is not available at the transmitter. Our new scheme is carried out in two steps. First, the transmitter selects the two strongest antennas based on the feedback from the receiver, which maximizes the instantaneous signal-to-noise ratio (SNR) of the transmitter-receiver channel. Second, the Alamouti scheme is employed at the selected antennas in order to perform data transmission. At the receiver and the eavesdropper, maximal-ratio combining is applied in order to exploit the multiple antennas. We derive a new closed-form expression for the secrecy outage probability in non-identical Rayleigh fading, and using this result, we then present the probability of non-zero secrecy capacity in closed form and the ε-outage secrecy capacity in numerical form. We demonstrate that our proposed TAS-Alamouti scheme offers lower secrecy outage probability than a single TAS scheme when the SNR of the transmitter-receiver channel is above a specific value.
We propose and analyze transmit antenna selection (TAS) to enhance physical layer security in a wiretap channel with multiple antennas at the transmitter, the receiver, and the eavesdropper. We consider the practical scenario of passive eavesdropping, where the transmitter does not have any channel state information (CSI) of the eavesdropper's channel. In the main channel between the transmitter and the receiver, we select a single antenna at the transmitter that maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver. At the receiver and the eavesdropper, we consider two combining techniques: 1) maximal-ratio combining (MRC) and 2) selection combining (SC). For non-identical Rayleigh fading between the main channel and the eavesdropper's channel, we first derive new closed-form expressions for the exact and asymptotic secrecy outage probabilities. The asymptotic results accurately reveal the secrecy diversity order and the secrecy array gain. Next, we derive new closed-form expressions for the probability of positive secrecy and characterize the ε-outage secrecy capacity. We show that, under TAS/MRC and TAS/SC protocols, the secrecy outage probability approaches zero and the ε-outage secrecy capacity increases with increasing number of transmitter antennas.
Antenna selection, which has been recognised as an important technique to enhance physical layer security in multiple-input multiple-output wiretap channels, requires low feedback overhead and low hardware complexity. In this chapter, antenna selection strategies in different application scenarios for wiretap channels are reviewed in order to demonstrate their benefits in terms of enhancing secure transmissions. The basic idea of transmit antenna selection (TAS) is first detailed and then TAS with Alamouti coding is provided. Furthermore, antenna selection strategies in full-duplex wiretap channels are presented, followed by a discussion on the impact of imperfect feedback and correlation on the secrecy performance of TAS.
The impact of feedback errors on the security performances of multiple-input multiple-output (MIMO) wiretap channels is analyzed. For the legitimate MIMO system, two schemes are considered: 1) transmit antenna selection/maximal ratio combining (TAS/MRC) and 2) transmit antenna selection/receive antenna selection (TAS/RAS). The eavesdropper uses either MRC or RAS techniques. We derive closed-form expressions for the exact secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (PSPSC). In order to obtain the secrecy diversity order and the secrecy array gain, on asymptotic SOP expression is also derived.
In this paper, a novel transmit antenna selection scheme for multiple input multiple output (MIMO) wiretap channels is proposed. This new scheme achieves higher secrecy performance by exploiting eavesdropper's channel state information. The key idea behind our proposal is to perform the antenna selection aiming to maximize the overall secrecy rate instead of maximizing the instantaneous signal-to-noise ratio of the main channel. In our analysis, we assume that the legitimate receiver and the eavesdropper employ a maximal-ratio combining technique to combine the received signals from the transmitter. Considering Nakagami-m fading channels, a closed-form expression for the secrecy outage probability is derived, which can be used as a quality of service metric. Further, an asymptotic analysis is carried out and the diversity/array gains are obtained. The ergodic secrecy rate is also investigated for the case of multiple input single output wiretap channels. Representative numerical examples are plotted and validated through Monte Carlo simulations. Insightful discussions are drawn from the proposed analysis.
In this paper, we exploit the potential benefits of machine learning in enhancing physical layer security in cooperative wireless networks. We focus on the case where multiple relays adopt amplify-and-forward (AF) relaying to forward information from the source to the destination. It is assumed that the global channel state information (CSI) of the legitimate links and wiretap links is available to the source. The optimal relay is selected to improve physical layer security against eavesdropping. By modeling the problem of the relay selection as a multi-class classification problem, a neural network (NN) based scheme is proposed to select the optimal relay which guarantees the perfect secrecy performance of the relay cooperative communication system. Compared with the conventional relay selection scheme, the simulation results show that our proposed scheme not only achieves almost the same secrecy performance, but also has advantage of relatively small feedback overhead. The work presented here provides insights into the new design of relay selection based on machine learning.