PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse Synchronization
PRONTO: Preamble Overhead Reduction With Neural Networks for Coarse Synchronization
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DOI：
10.1109/twc.2023.3256961
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发表时间：
2021-12
影响因子：
10.4
通讯作者：
N. Soltani;Debashri Roy;K. Chowdhury
中科院分区：
文献类型：
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作者：
N. Soltani;Debashri Roy;K. Chowdhury
在基于IEEE 802.11 WiFi的波形中，接收器使用被称为传统短训练字段（L-STF）的前导码的第一字段来执行粗略的时间和频率同步。L-STF占用了前导码长度的40%，占用了32\mu \text{s}$的通话时间。为了减少通信开销，我们提出了一种改进的波形，其中通过消除L-STF来减少前导码长度。为了解码这种修改后的波形，我们提出了一种称为PRONTO的基于神经网络（NN）的方案，该方案使用其他前导字段（特别是传统的长训练字段（L-LTF））执行粗略的时间和频率估计。我们的贡献有三个方面：（i）我们介绍了PRONTO，该PRONTO具有用于数据包检测和粗略载波频率偏移（CFO）估计的定制卷积神经网络（CNN），沿着用于鲁棒训练的数据增强步骤。(ii)我们提出了一个广义的决策流程，使PRONTO兼容传统的波形，包括标准的L-STF。(iii)我们验证的结果在空中无线数据集从软件定义无线电（SDR）的测试平台。我们的评估表明，PRONTO可以执行数据包检测与100%的准确性，和粗CFO估计误差小至3%。我们证明，PRONTO提供了高达40%的前导码长度减少，没有误码率（BER）退化。我们进一步表明，PRONTO能够在新的环境中实现相同的性能，而无需重新训练CNN。最后，我们通过实验证明了PRONTO通过GPU并行化实现的加速比。
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 $32 \mu \text{s}$ 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.