Machine Learning Based Featureless Signalling

TL;DR

This paper introduces a machine learning approach to generate featureless, noise-like spread signals for DSSS systems, enhancing stealth and performance against detection and jamming.

Contribution

It presents a novel machine learning scheme that creates non-repetitive, low-detectability signals with improved processing gain and uncoordinated synchronization capabilities.

Findings

Generated signals have low probabilities of detection and interception

Enhanced processing gain compared to standard DSSS

Supports uncoordinated synchronization methods

Abstract

Direct-sequence spread-spectrum (DSSS) is commonly used to mitigate the effect of jamming and to operate under an adversary receiver's thermal noise floor in order to avoid signal detection. Unfortunately, the discrete nature and unique distribution of DSSS spreading sequences make it relatively easy to detect the resulting transmitted signals. To overcome this issue, this paper proposes a machine learning based scheme that generates featureless, non-repetitive noise-like spread signals. The proposed scheme provides several benefits over the standard DSSS system including the ability to generate signals with low probabilities of detection/intercept, additional processing gain and also an uncoordinated synchronisation method.