Abstract
Perfect channel state information (CSI) is required in most of the classical physical-layer security techniques, while it is difficult to obtain the ideal CSI due to the time-varying wireless fading channel. Although imperfect CSI has a great impact on the security of MIMO communications, deep learning is becoming a promising solution to handle the negative effect of imperfect CSI. In this work, we propose two types of deep learning-based secure MIMO detectors for heterogeneous networks, where the macro base station (BS) chooses the null-space eigenvectors to prevent information leakage to the femto BS. Thus, the bit error rate of the associated user is adopted as the metric to evaluate the system performance. With the help of deep convolutional neural networks (CNNs), the macro BS obtains the refined version from the imperfect CSI. Simulation results are provided to validate the proposed algorithms. The impacts of system parameters, such as the correlation factor of imperfect CSI, the normalized doppler frequency, the number of antennas is investigated in different setup scenarios. The results show that considerable performance gains can be obtained from the deep learning-based detectors compared with the classical maximum likelihood algorithm.
Highlights
In recent years, the heterogeneous wireless network, which can support high-density and high-rate traffic, has attracted much research interest from both academic and industry sectors [1,2,3]
A training data set with 10000 batches as well as a validation data set with 1000 batches are fed to the deep CNN (DCNN), and a test data set with 1000 batches is used to evaluate the bit error rate (BER) performance of the proposed schemes
We investigate two types of deep learning-based secure multiple-input multiple-output (MIMO) detectors for heterogeneous networks
Summary
The heterogeneous wireless network, which can support high-density and high-rate traffic, has attracted much research interest from both academic and industry sectors [1,2,3]. As an improvement, deep learning-based secure communication has been proposed to protect the message transmission to the legitimate users [15]. There are some works on learning-based channel estimation algorithms, with imperfect CSI, the impacts of CNN-based MIMO detector on physical-layer security performance is still an open question. In this paper, we will discuss the deep learning-based secure MIMO communication algorithm for heterogeneous networks with imperfect CSI. The receiver obtains the CSI through pilots signal transmitted from the base station, and there exists a time difference between the channel estimation and the data packet transmission. We employ the deep learning-based technique for secure MIMO communications in heterogeneous networks, which can exploit the benefits of CNN learning model to produce more accurate CSI and reduce the bit error rate (BER) of the receiver. Notations: We use CN (μ, σ2 ) to represent the circularly symmetric complex Gaussian random variable with mean μ and variance σ2 , f X ( x ) and FX ( x ) denote the probability density function (PDF) and cumulative distribution function (CDF) of a random variance x, respectively, diag(A) is a row vector consisting of all diagonal elements of A, A∗ is the conjugate transpose of the A, and H FM denotes the wireless channel fading matrix from M to F
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