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IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS)

Physical Layer Secret Key Generation for FDD-OTFS Systems

Aoba Morimoto, Shun Kojima

Published Aug 1, 2026• 0 citations
Publisher
DOI
10.1109/apwcs71511.2026.11742852

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Physical layer key generation techniques aim to extract secret keys from the information contained in wireless channels. However, existing key generation schemes often rely on time-frequency domain waveforms for channel estimation, which not only makes secret extraction less reliable but may also compromise the confidentiality of the extracted secret information. This paper presents physical layer key generation methods relying on the Orthogonal Time Frequency and Space (OTFS) waveform. We present analysis showing that the delay-Doppler domain channel estimates obtained using OTFS are conducive to more secure and reliable secret extraction than time-frequency domain channel estimates obtained using the prevalent Orthogonal Frequency Division Multiplexing (OFDM). This analysis provides theoretical guarantees under certain simple assumptions. We then relax those assumptions in extensive time-division duplex (TDD) simulations and show that under realistic settings, OTFS offers the expected benefits to reliability and security. Our simulations show that the introduced OTFS schemes can reliably extract secret keys from channel estimates in scenarios where time-frequency domain methods deteriorate.
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).