Deep Learning-Based Secure MIMO Communications with Imperfect CSI for Heterogeneous Networks

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.

Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System

“…Therefore, for all the attacks in the dataset, we strive for highly optimal training data in future study. Most classical physical-layer security techniques require CSI, but it is hard to obtain due to time-varying wireless declining [ 37 , 38 ]. As one of the most serious cyber attacks, APT caused global concern.…”

Energy-Efficient Power Allocation and User Association in Heterogeneous Networks with Deep Reinforcement Learning

“…However, the growing complexity of wireless networks due to increased links and heterogeneous network structure create tremendous challenges for system designs, thus calling for more intelligent techniques for effective yet efficient resource management strategies. In this perspective, data-driven machine learning techniques have been regarded as viable new approaches to dealing with complex network dynamics [5][6][7][8][9][10]. Compared with traditional model-based algorithms [11][12][13][14], deep reinforcement learning (DRL), leveraging recent advances in deep neural networks with reinforcement learning [15][16][17], can autonomously extract features from the raw data with different formats and complex correlations experienced by the mobile environments.…”

Application of Reinforcement Learning and Deep Learning in Multiple-Input and Multiple-Output (MIMO) Systems

“…A DL approach for secure MIMO communications has been employed in [ 192 ] by exploiting the advantage of CNN learning network to generate more accurate CSI and to reduce the BER of the receiver. Both the ideal CSI and imperfect CSI are included in the training set that then may be used in different scenarios.…”