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Autoencoder-Based Unequal Error Protection Codes
[article]
2021
arXiv
pre-print
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Ninkovic are protected differently [6] . ...
Vukobratovic are with the Department of Power, Electronics and Communications Engineering, University of Novi Sad, 21000, Novi Sad, Serbia (e-mail: {ninkovic, dejanv}@uns.ac.rs). C. Häger, H. ...
arXiv:2104.08190v1
fatcat:4mt3awgh7bb4dnbtq75trdh5sy
Preamble-Based Packet Detection in Wi-Fi: A Deep Learning Approach
[article]
2020
arXiv
pre-print
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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
arXiv:2009.05740v1
fatcat:3sh2sjoea5f6xlcrkd24egxiiy
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... 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.
Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah
2021
IEEE Access
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Ninkovic et al.: Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah FIGURE 14. ...
doi:10.1109/access.2021.3096853
fatcat:hasdswe7djfpnh77gg4535axne
Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah
[article]
2021
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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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
arXiv:2004.11716v2
fatcat:xf5k27hddfgwpgqjkt3dhtauoe