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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

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Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems.

Shengjie Liu¹, Guo Wei², Haoyu He¹

¹School of Computer Science, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This study introduces a low-overhead physical layer secret key generation (PLKG) method using reconfigurable intelligent surfaces (IRS) in multi-input multi-output (MIMO) systems. It leverages deep learning to efficiently extract channel reciprocity, improving key generation rates and security.

Keywords:

deep learning intelligent reflecting surface physical layer secret key generation

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Area of Science:

Wireless Communications
Information Security
Artificial Intelligence

Background:

Physical layer secret key generation (PLKG) is crucial for secure communications but is often limited by ideal environments and interference.
Existing reconfigurable intelligent surface (IRS)-assisted PLKG schemes primarily focus on single-input-single-output (SISO) systems, which are insufficient for future multi-input multi-output (MIMO) networks.
MIMO systems introduce significant overhead for channel reciprocity extraction, a challenge that needs addressing for effective PLKG.

Purpose of the Study:

To propose a novel, low-overhead IRS-assisted PLKG scheme tailored for MIMO communication environments.
To address the challenge of high overhead in channel reciprocity extraction within MIMO systems.
To enhance the performance of PLKG in terms of key generation rate, error rate, and randomness.

Main Methods:

Combining direct and IRS-reflecting channels to construct the channel response function.
Developing a theoretically optimal interaction matrix to approximate the optimal achievable rate.
Designing a deep learning model, IRS-CRNet (IRS-assisted Channel Reciprocity-learning Neural Network), for efficient channel reciprocity extraction in Time Division Duplexing (TDD) systems.
Implementing a PLKG scheme based on the IRS-CRNet.

Main Results:

The proposed IRS-CRNet effectively extracts channel reciprocity in MIMO systems.
The IRS-assisted PLKG scheme demonstrates superior performance compared to existing methods.
Simulation results validate the scheme's effectiveness in key generation rate, key error rate, and randomness.

Conclusions:

The novel low-overhead IRS-assisted PLKG scheme using deep learning is effective for MIMO environments.
IRS-CRNet significantly reduces the overhead associated with channel reciprocity extraction in TDD-MIMO systems.
This approach enhances the security and efficiency of secret key generation in advanced wireless networks.