Robust RF Mixture Signal Recognition Using Discriminative Dictionary Learning

Chen, Hao; Kim, Seung-Jun

doi:10.1109/access.2021.3120635

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…For a study involving multiple signals, single label signals (one signal at a time) as well as mixture signals (all possible signal combinations of single-label signals) have to be generated which is a cumbersome task. In [81], a discriminative dictionary learning (DL) algorithm is proposed for the multi label signal identification by using single label data (BL, BLE, FHSS1, FHSS2, WiFi1, WiFi2). The learned features are classified using ZF (zero-forcing), MF (matched filter), LR (logistic regression), SVM and VOLUME 10, 2022…”

Section: B Spectral Signature Analysis Based Detection Techniquesmentioning

confidence: 99%

Coexistence Management in Wireless Networks-A Survey

Hasan

Khan

2022

IEEE Access

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The rapid proliferation of wireless networks poses a great challenge of effective coexistence management amongst a plethora of wireless communication protocol users that are co-located and contending for the ever scarce spectrum available. For effective spectrum utilization and optimum performance of existing wireless networks and for the realization of new wireless networks, coexistence management of the wireless spectrum is the key to ensure the performance of multiple wireless networks in close proximity. This paper provides a comprehensive review of state-of-the-art coexistence protocols in wireless networks. The paper not only discusses wireless interference detection techniques in detail but also provides various coping mechanisms to counter the interference.

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Section: B Spectral Signature Analysis Based Detection Techniquesmentioning

confidence: 99%

Coexistence Management in Wireless Networks-A Survey

Hasan

Khan

2022

IEEE Access

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“…The baseband complex signals of 40 MHz bandwidth were acquired inside a RF shield box. In total, 6 signal types were used, namely 2 types of Wi-Fi signals, Bluetooth (BT), Bluetooth Low Energy (BLE), and 2 types of frequency hopping spread spectrum (FHSS) transmissions [6]. The 2 types of Wi-Fi signals correspond to the Wi-Fi transmission in a high occupancy scenario (denoted as "Wi-Fi1" in the sequel), capturing intensive Wi-Fi usage such as downloading a large file, and in a low occupancy scenario (Wi-Fi2), representing a more sporadic use case.…”

Section: A Experiments Setupmentioning

confidence: 99%

“…The power spectrum of the received signal was clustered over time and the 4th-order spectrum was analyzed using three-way tensor decomposition in [5]. Mixed signals were classified using dictionaries trained on the singletransmitter scenes in [6]. However, the unique time-frequency patterns of the constituent signals have not been fully exploited in a machine learning framework.…”

Section: Introductionmentioning

confidence: 99%