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.

翻译：在 IEEE 802.11 WiFi 基于 WiFi 的波形中,接收器使用被称为遗留短培训字段(L-STF)的序言第一字段,进行粗略的时间和频率同步。L-STF占用了40%的序言长度,并占用了32个空气时间。为了减少通信管理费,我们建议修改波形,通过取消L-STF来缩短其序言长度。为了解码这个修改过的波形,我们提议一个称为PRONTO的机器学习(ML)方案,利用其他序言字段,特别是遗留的长培训字段(L-LTF),进行粗略的时间和频率估算。我们的贡献有三重:(一) 我们介绍PRONTO,以定制的革命神经神经网络网络网络(CNNNs)为定制,用于进行包检测和粗略的CFO估计,以及数据增强步骤,用于进行强力培训。 (二) 我们提议一个使PRONTO与包括标准L-STF的遗留波形波形波形波形图兼容性估算结果,我们通过测试的C-BRBRMFO 展示了40的精确度评估结果。