Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach
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Vukan Ninkovic, Dejan Vukobratovic, Aleksandar Valka, Dejan Dumic
2020
Abstract
Wi-Fi systems based on the family of IEEE 802.11 standards that operate in
unlicenced bands are the most popular wireless interfaces that use Listen
Before Talk (LBT) methodology for channel access. Distinctive feature of
majority of LBT-based systems is that the transmitters use preambles that
precede the data to allow the receivers to acquire initial signal detection and
synchronization. The first digital processing step at the receiver applied over
the incoming discrete-time complex-baseband samples after analog-to-digital
conversion is the packet detection step, i.e., the detection of the initial
samples of each of the frames arriving within the incoming stream. Since the
preambles usually contain repetitions of training symbols with good correlation
properties, conventional digital receivers apply correlation-based methods for
packet detection. Following the recent interest in data-based deep learning
(DL) methods for physical layer signal processing, in this paper, we challenge
the conventional methods with DL-based approach for Wi-Fi packet detection.
Using one-dimensional Convolutional Neural Networks (1D-CNN), we present a
detailed complexity vs performance analysis and comparison between conventional
and DL-based Wi-Fi packet detection approaches.
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