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

Area of Science:

• Wireless Communications
• Information Security
• Machine Learning

Background:

• Classical physical-layer security requires perfect channel state information (CSI), which is challenging to obtain in dynamic wireless environments.
• Imperfect CSI significantly degrades the security of Multiple-Input Multiple-Output (MIMO) systems.
• Deep learning offers a promising approach to mitigate the adverse effects of imperfect CSI on communication security.

Purpose of the Study:

• To propose novel deep learning-based secure MIMO detectors for heterogeneous networks.
• To enhance physical-layer security by preventing information leakage from macro base stations (BS) to femto BS.
• To evaluate system performance using the bit error rate (BER) of associated users.

Main Methods:

• Development of two types of deep learning-based secure MIMO detectors.
• Utilizing deep convolutional neural networks (CNNs) for refining imperfect CSI at the macro BS.
• Employing null-space eigenvectors by the macro BS to obstruct information leakage.

Main Results:

• Deep learning-based detectors demonstrate considerable performance gains over the classical maximum likelihood algorithm.
• The effectiveness of the proposed algorithms is validated through simulations.
• The study investigates the impact of system parameters like CSI correlation, Doppler frequency, and antenna count on performance.

Conclusions:

• Deep learning effectively addresses the challenges posed by imperfect CSI in secure MIMO communications.
• The proposed deep learning detectors offer a robust solution for enhancing security in heterogeneous wireless networks.
• Significant performance improvements are achievable using deep learning for secure MIMO detection.