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IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN)

Machine Learning Enhanced Near-Field Secret Key Generation for Extremely Large-Scale MIMO

Chen Chen, Junqing Zhang

Published May 5, 2024• 1 citation
Publisher

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.

DOI
10.1109/icmlcn59089.2024.10624801

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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.
This paper investigates an artificial randomness-based key generation scheme in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) communications, aiming to overcome the limitations of static wireless chan-nels in generating secret keys. The proposed approach introduces artificial randomness via the pre coding vector, enabling legitimate users to generate secret keys while preventing eavesdropping. To maximize the secret key rate (SKR), the singular value decomposition (SVD) method is utilized to identify the legitimate subspace for generating secret keys and its orthogonal subspace to thwart eavesdropping. Additionally, a Dinkelbach method-based power allocation algorithm is designed to inject noise into both the legitimate subspace and its orthogonal subspace. Our simulation results validate the efficiency of the proposed key generation scheme, demonstrating its superiority over existing approaches.
With the substantial increase in the number of antennas, polar-domain channel modeling for extremely large-scale antenna array (ELAA) systems has been introduced to capture both angular and distance information in near-field environments. The fine-grained polar-domain channel provides additional sources of randomness, making it well-suited for physical layer key generation (PLKG). To minimize the pilot overhead in multi-user key generation and leverage the randomness from the polar-domain channel paths, we herein design a zero-forcing (ZF)-based precoding scheme to mitigate inter-path and inter-user interference. Using ZF precoding, we derive an analytical expression for the sum secret key rate (SKR) as a function of power allocation variables, and then optimize these variables in the presence of eavesdroppers. Our simulations validate the proposed precoding design and power allocation methods in terms of sum SKR versus the transmit power, antenna configurations, and spatial correlation between legitimate and eavesdropping channels.
In this work, we present a scheme of physical layer secret key generation for Millimeter wave (mmWave) Massive MIMO system. Our scheme is compatible with current hardware structure and protocols including Analog Beamforming, Massive MIMO, and Beam Sweep. We add a small perturbation angle into the Angle of Arrival (AoA) of the transmitter as the common randomness, which significantly improved the secret key rate without being constrained by the complexity of link initialization protocols and low dynamic of the channel. Therefore, the secret key rate can be enhanced by increasing the number of perturbations. In addition, our scheme can combat co-located eavesdropper (Eve) by utilizing the high directionality of Massive MIMO antenna. Numerical results show that our scheme has a high bit agreement ratio (BAR) between legitimate users while the co-located Eve only gets the BAR around 50%, which indicates that the secrecy of the generated key is well achieved.
Extremely large-scale multiple-input and multiple-output (XL-MIMO) is one of the most promising key technologies for the coming sixth generation wireless communications. Considering passive eavesdropping threats, the influence of near-field channel characteristics of uplink multi-user XL-MIMO systems on the physical layer security schemes are analyzed in this paper. Then, a comprehensive survey of physical layer encrypted methods and low complexity detection methods is presented. Conclusively, it is pointed out that the study on low complexity and high reliability modulation encrypted methods based on near field channel characteristics for uplink multi-user XL-MIMO systems has very important research value for the next generation's green security communications. Some corresponding future potential research works for low complexity physical layer security schemes based on channel characteristics of near-field XL-MIMO systems are also presented in this paper.
Physical layer key generation in multi-user multiple-input multiple-output (MU-MIMO) system is challenging due to the poor randomness of the equivalent channel response. The precoding matrix combines the channels from different antennas in a constructive way, resulting in a flat fading equivalent channel. In this paper, we proposes new physical layer key generation schemes based on precoding adjustment. Two strategies are proposed to enhance the randomness of the equivalent channel response at the cost of beamforming gain, which consist in adjusting random elements and adjusting the largest elements. When considering the transmission of artificial noise (AN) at the base station, random phase adjustment strategy is proposed to the precoding scheme which does not affect the beamforming gain. The signal-to-interference-plus-noise ratio of user and key generation performance are simulated. Simulation results show that adjusting the precoding elements with larger amplitudes is more efficient to increase the information entropy of the channel response. When the system is able to transmit an AN with high power ratio, random phase adjustment can help to generate secret keys with higher information entropy compared to two other strategies.
Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.