# PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization

arXiv: https://arxiv.org/abs/2112.10885 · HTML: https://mlsanity.com/paper/2112.10885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Communication Algorithms via Deep Learning](https://mlsanity.com/paper/1805.09317/) (2018)
- [End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication](https://mlsanity.com/paper/2009.05261/) (2020)
- [ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks](https://mlsanity.com/paper/1812.01124/) (2018)
- [Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa](https://mlsanity.com/paper/2107.02867/) (2021)
- [Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces](https://mlsanity.com/paper/2006.02201/) (2020)
- [Radio Frequency Fingerprint Identification for LoRa Using Spectrogram and CNN](https://mlsanity.com/paper/2101.01668/) (2020)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [A Neural Network Aided Approach for LDPC Coded DCO-OFDM with Clipping Distortion](https://mlsanity.com/paper/1809.01022/) (2018)
- [Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah](https://mlsanity.com/paper/2004.11716/) (2020)
- [Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach](https://mlsanity.com/paper/2009.05740/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning from the Best: Active Learning for Wireless Communications](https://mlsanity.com/paper/2402.04896/) (2024)

## Abstract (arXiv)

In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.