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Channel Feature Extraction and Physical Layer Key Generation in FDD Massive MIMO Systems

Xinzhu Hu

Published Jan 1, 2026• 0 citations
Publisher

Abstract

While Physical Layer Key Generation (PKG) offers lightweight security for 6G IoT, channel non-reciprocity in FDD massive MIMO degrades key consistency. This paper presents a comprehensive review of Physical Layer Key Generation (PKG) techniques within Frequency Division Duplex (FDD) massive MIMO systems. While PKG leverages the inherent randomness of wireless channels to establish lightweight security for communication nodes, FDD systems pose a significant challenge. The separated uplink and downlink bands produce non-reciprocal fast-fading channel responses, which inherently degrade the consistency of keys generated from instantaneous channel state information (CSI). To address this, this study categorizes existing methodologies into three primary routes: stable propagation feature extraction, intelligent feature mapping, and system engineering optimization. It examines their mechanisms across the angle, beam, and statistical covariance domains, comparing representative schemes in terms of Key Disagreement Rate (KDR), overhead, and computational complexity. Ultimately, future research must integrate physical priors with data-driven models to ensure robust security in practical 6G networks.

DOI
10.1051/itmconf/20269101037

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While Physical Layer Key Generation (PKG) offers lightweight security for 6G IoT, channel non-reciprocity in FDD massive MIMO degrades key consistency. This paper presents a comprehensive review of Physical Layer Key Generation (PKG) techniques within Frequency Division Duplex (FDD) massive MIMO systems. While PKG leverages the inherent randomness of wireless channels to establish lightweight security for communication nodes, FDD systems pose a significant challenge. The separated uplink and downlink bands produce non-reciprocal fast-fading channel responses, which inherently degrade the consistency of keys generated from instantaneous channel state information (CSI). To address this, this study categorizes existing methodologies into three primary routes: stable propagation feature extraction, intelligent feature mapping, and system engineering optimization. It examines their mechanisms across the angle, beam, and statistical covariance domains, comparing representative schemes in terms of Key Disagreement Rate (KDR), overhead, and computational complexity. Ultimately, future research must integrate physical priors with data-driven models to ensure robust security in practical 6G networks.
Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency-division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal anymore. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This article proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then, a key generation neural network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this article can be used for resource-constrained IoT devices in FDD systems.
Physical-layer secret key generation (PSKG) stands as a promising privacy protection technique, establishing shared encryption keys through the analysis of highly correlated wireless channel measurements. This approach relies on exploiting reciprocal channel characteristics between uplink and downlink transmissions. Nonetheless, the distinct carrier frequencies employed for uplink and downlink in frequency-division duplexing (FDD) systems pose a challenge in identifying common features. This paper presents a novel approach that exploits the inherent reciprocity between scattering parameters of passive two-port networks within same frequency ranges to overcome this obstacle. By capitalizing this reciprocity and considering closely situated FDD bands, a seamless continuity is anticipated in phase differences extracted form the corresponding S-parameters, between neighboring antennas of an antenna array from both uplink and downlink directions. This continuity, thereby ensures consistency in the generated keys from both transmission ends. Furthermore, a two-stage pre-processing method is proposed to enhance performance effectively. Additionally, the paper suggests the utilization of polynomial curve-fitting through measurement data to improve reciprocity and proposes a non-linear framework for quantizing the merging points of the two FDD bands. A statistical analysis employing multiple linear regression is provided to determine the error probability associated with the generated keys. Empirical results validate the feasibility and effectiveness of the proposed key generation scheme, affirming its attributes in terms of randomness, efficiency, key distribution uniformity, and key disagreement ratio (KDR).
Physical Layer Secret Key Generation (PLSKG), which utilizes wireless channel reciprocity, is increasingly adopted to secure wireless communications. In Time Division Duplexing (TDD) systems, the inherent channel reciprocity facilitates the generation of cryptographic keys without requiring a key exchange process. Conversely, Frequency Division Duplexing (FDD) systems present greater challenges for key generation due to the use of distinct frequency bands for uplink and downlink, resulting in differing frequency responses. In this paper, we address this challenge by modeling the channel along with connecting cables as a two-port network and utilizing the reciprocity principles applicable to such networks within the same frequency range. For key generation, we construct the channel profile for each FDD band by utilizing the amplitude of the bidirectional scattering parameters S12and S21in two closely spaced FDD bands, thereby ensuring the required reciprocity in FDD systems. To assess the practicality of the proposed scheme, we analyze its performance across various indoor scenarios and compare it with other key generation schemes using multiple performance indicators. The comparative results confirm the feasibility and effectiveness of our scheme in terms of randomness and key disagreement ratio.
The utilization of physical layer Secret Key Generation (SKG) is increasingly prevalent in securing wireless communication within Internet of Things systems, particularly in Narrow-band IoT. While most key-generation schemes are tailored for Time Division Duplex (TDD) systems, generating secret keys in Frequency Division Duplex (FDD) systems presents challenges due to the distinct frequency bands for uplink and downlink. In response to this, we propose an efficient FDD-based key generation technique that capitalizes on the reciprocity of scattering matrix parametersS12andS21within the same frequency range. Specifically, we harness the capabilities of MUSIC algorithm during the direction of arrival estimation phase. Numerical results demonstrate promising outcomes in addressing key challenges, including randomness, and Key Disagreement Ratio (KDR).
This chapter presents a multi-user secret key generation in massive multiple-input multiple-output (MIMO) wireless networks. Physical-layer key generation (PKG) based on channel reciprocity has emerged as a new technique to establish secret keys between devices. However, in future wireless communications, massive antennas will be employed to support multiple users, which bring new challenges to PKG, e.g., low key agreements caused by long-time delay or strong inter-user interference. By harnessing the sparse characteristics of the beam domain channel model, we propose a general channel dimensionality reduction (CDR)-based secret key generation scheme and optimize the parameters of this scheme. The security of this key generation scheme under different user cases of non-overlapping and overlapping beams is then discussed. Some new directions that generalize and improve the basic scenarios for PKG are described. These new directions include multi-cell multi-user secret key generation, PKG in millimeter wave wireless communications, and PKG in frequency division duplexing systems.
Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.