# Deep Learning based Blind Symbol Packing Ratio Estimation for Faster-than-Nyquist Signaling

## TL;DR

This paper introduces a novel deep learning-based blind estimation method for symbol packing ratio in FTN signaling, enabling adaptive transmission and revealing security vulnerabilities.

## Contribution

It presents the first effective deep learning approach for blind symbol packing ratio estimation in FTN signaling, improving robustness and enabling new transmission strategies.

## Key findings

- Fast convergence and robustness to SNR demonstrated
- Enables adaptive FTN transmission without dedicated control
- Reveals security vulnerabilities in FTN communications

## Abstract

This letter proposes a blind symbol packing rartio estimation for faster-than-Nyquist (FTN) signaling based on state-of-the-art deep learning (DL) technology. The symbol packing rartio is a vital parameter to obtain the real symbol rate and recover the origin symbols from the received symbols by calculating the intersymbol interference (ISI). To the best of our knowledge, this is the first effective estimation approach for symbol packing rartio in FTN signaling and has shown its fast convergence and robustness to signal-to-noise ratio (SNR) by numerical simulations. Benefiting from the proposed blind estimation, the packing-ratio-based adaptive FTN transmission without dedicate channel or control frame becomes available. Also, the secure FTN communications based on secret symbol packing rartio can be easily cracked.