Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach

TL;DR

This paper introduces a deep learning approach using 1D-CNNs for Wi-Fi packet detection, challenging traditional correlation-based methods with a detailed analysis of performance and complexity.

Contribution

The paper presents the first application of deep learning for Wi-Fi packet detection, offering a novel approach that improves detection performance over conventional methods.

Findings

DL-based detection outperforms traditional correlation methods

Analysis shows favorable complexity-performance trade-offs

Deep learning enhances robustness in packet detection

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.