  Wi-Fi systems based on the IEEE 802.11 standards are the most popular
wireless interfaces that use Listen Before Talk (LBT) method for channel
access. The distinctive feature of a majority of LBT-based systems is that the
transmitters use preambles that precede the data to allow the receivers to
perform packet detection and carrier frequency offset (CFO) estimation.
Preambles usually contain repetitions of training symbols with good correlation
properties, while conventional digital receivers apply correlation-based
methods for both packet detection and CFO estimation. However, in recent years,
data-based machine learning methods are disrupting physical layer research.
Promising results have been presented, in particular, in the domain of deep
learning (DL)-based channel estimation. In this paper, we present a performance
and complexity analysis of packet detection and CFO estimation using both the
conventional and the DL-based approaches. The goal of the study is to
investigate under which conditions the performance of the DL-based methods
approach or even surpass the conventional methods, but also, under which
conditions their performance is inferior. Focusing on the emerging IEEE
802.11ah standard, our investigation uses both the standard-based simulated
environment, and a real-world testbed based on Software Defined Radios.
