Chen, Chen and Zhang, Junqing 
ORCID: 0000-0002-3502-2926
(2024)
Machine Learning Enhanced Near-Field Secret Key Generation for Extremely Large-Scale MIMO.
In: 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), 2024-5-5 - 2024-5-8.
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ICMLCN 2024 KeyGen_XLMIMO.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (739kB) | Preview |
Abstract
The next generation of communication systems are expected to operate at high frequency bands such as millimetre wave (mmWave) and terahertz (THz) bands, and use extremely large-scale multiple-input-multiple-output (XL-MIMO). This brings a paradigm shift from far-field to near-field communications. In this paper, we investigate physical-layer key generation in near-field XL-MIMO communications and focus on the most challenging line-of-sight (LoS) propagation scenario. To be specific, we introduce artificial randomness to enhance secret key generation and enable theoretical analysis of secret key rate (SKR). We provide the zero-forcing (ZF) precoding solution that can null the received signal at the eavesdropper. We show that the ZF precoding leads to a low SKR in challenging scenarios of low transmit powers and small eavesdropping distances. To improve the SKR in these challenging scenarios, we propose a novel low-complexity machine learning-based beam focusing (MLBF) scheme. Simulation results show that the proposed MLBF scheme achieves a higher SKR than the benchmark methods.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | 4613 Theory Of Computation, 46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering, Machine Learning and Artificial Intelligence |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 29 Jan 2024 08:35 |
| Last Modified: | 11 Dec 2024 14:08 |
| DOI: | 10.1109/icmlcn59089.2024.10624801 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3178044 |
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