Autoencoder-Based Gaussianity for Low Probability of Intercept Detection in Terahertz Communications

Abigail O. Oyekola, Majumder Haider, Imtiaz Ahmed, Danda B. Rawat

Abstract: This paper addresses the challenges of signal interception and detection in Terahertz (THz) networks. We explore how preserving the Gaussianity of transmit signals enhances Low Probability of Intercept and Detection (LPID) features using a data-driven autoencoder in THz band communication systems. By analyzing the probability distribution function (PDF) of THz band transmit signals, we evaluate their impact on LPID performance. Given the high sensitivity of THz systems to Phase Noise (PN) and the In-phase and Quadrature (IQ) imbalance impairments, we illustrate the importance of maintaining Gaussianity under varying PN conditions to ensure reliable and secure LPID transmission. Through comprehensive simulations, we demonstrate that PN does not compromise the Gaussianity of autoencoder-based THz band transmit signals. These results highlight the robustness of Gaussianity preservation, offering critical insights for designing THz band autoencoder-based LPID networks resilient to eavesdropping, jamming, and unauthorized interception. Our proposed autoencoder framework not only enhances LPID but also mitigates PN and IQ imbalance impairments, paving the way for secure, efficient, and reliable communication in next-generation THz networks.

External IDs:dblp:conf/milcom/OyekolaHAR25