TY  - EJOU
AU  - Huang, Jian 
AU  - Li, Mingwei 
AU  - Tian, Yulong 
AU  - Yao, Yi 
AU  - Han, Hao 

TI  - Design, Realization, and Evaluation of Faster End-to-End Data Transmission over Voice Channels
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 1
SN  - 1546-2226

AB  - With the popularization of new technologies, telephone fraud has become the main means of stealing money and personal identity information. Taking inspiration from the website authentication mechanism, we propose an end-to-end data modem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity. Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers. For example, speech activity detection may quickly classify encoded signals as non-speech signals and reject input waveforms. To address this issue, we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency, shape, and phase, alongside a lightweight MobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices. This method leverages the unique characteristics of linear frequency modulation signals, making them more easily transmitted and decoded in speech channels. To ensure reliable data delivery over unstable voice links, we further introduce a robust framing scheme with delimiter-based synchronization, a sample-level position remedying algorithm, and a feedback-driven retransmission mechanism. We have validated the feasibility and performance of our system through expanded real-world evaluations, demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate. This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels, which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.
KW  - Deep learning; modulation; chirp; data over voice

DO  - 10.32604/cmc.2025.073201