PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization

• URL: http://arxiv.org/abs/2112.10885v1
• Date: Mon, 20 Dec 2021 22:18:28 GMT
• Title: PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization
• Authors: Nasim Soltani, Debashri Roy, and Kaushik Chowdhury
• Abstract summary: 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) 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 machine learning (ML)-based scheme called PRONTO that performs coarse time and frequency estimations.
• Score: 1.242591017155152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 machine learning (ML)-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 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. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.

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