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.

翻译：在基于IEEE 802.11 WiFi的波形中，接收端利用前导码的第一个字段——传统短训练字段（L-STF）进行粗时间和频率同步。该字段占据前导码长度高达40%，占用长达32微秒的传输时间。为降低通信开销，我们提出了一种改进波形，通过移除L-STF来缩短前导码长度。针对该改进波形的解码问题，我们提出了一种基于神经网络（NN）的方案PRONTO，利用其他前导码字段（特别是传统长训练字段L-LTF）实现粗时间和频率估计。本文贡献包含三部分：（i）提出PRONTO方案，采用定制化卷积神经网络（CNN）进行数据包检测和粗载波频率偏移（CFO）估计，并设计数据增强步骤以实现稳健训练；（ii）提出通用决策流程，使PRONTO兼容包含标准L-STF的传统波形；（iii）基于软件无线电（SDR）测试平台的空中WiFi数据集验证效果。评估结果表明：PRONTO可实现100%准确率的包检测，粗CFO估计误差低至3%。该方案可缩减高达40%的前导码长度且不造成误码率（BER）退化，同时在新环境中无需重新训练CNN即可保持同等性能。最后，实验证明通过GPU并行化，PRONTO相比纯CPU实现取得了显著的加速效果。