  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.
