Featureless Wireless Communications using Enhanced Autoencoder
Ruhui Zhang, Wei Lin, Binbin Chen

TL;DR
This paper presents an autoencoder-based wireless communication system that generates featureless signals with low detectability and interception probability, improving security and reliability through novel loss functions and error correction integration.
Contribution
It introduces a new loss function with KL divergence to enhance noise-like signal characteristics and supports long message blocks with error correction coding, advancing featureless signal generation in wireless communications.
Findings
Enhanced noise-like signal properties with the new loss function.
Improved block error rate performance over traditional methods.
Validated system effectiveness in over-the-air experiments.
Abstract
Artificial intelligence (AI) techniques, particularly autoencoders (AEs), have gained significant attention in wireless communication systems. This paper investigates using an AE to generate featureless signals with a low probability of detection and interception (LPD/LPI). Firstly, we introduce a novel loss function that adds a KL divergence term to the categorical cross entropy, enhancing the noise like characteristics of AE-generated signals while preserving block error rate (BLER). Secondly, to support long source message blocks for the AE's inputs, we replace one-hot inputs of source blocks with binary inputs pre-encoded by conventional error correction coding schemes. The AE's outputs are then decoded back to the source blocks using the same scheme. This design enables the AE to learn the coding structure, yielding superior BLER performance on coded blocks and the BLER of the…
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Taxonomy
TopicsWireless Signal Modulation Classification · Wireless Communication Security Techniques · Advanced Wireless Communication Techniques
