Hardware platform of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF signal activity detection

Pelissier, Michaël; Masson, Gilles; Ouvry, Laurent; Dias, Luis Felipe Fonseca; Marnat, Marguerite

doi:10.1109/iscas.2018.8351834

Cited by 6 publications

(12 citation statements)

References 11 publications

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“…On the other hand, AICs present front-ends that include signal generators, mixers and filters. In recent prototypes, able to perform high compression and ultra-wideband spectrum sensing [ 8 , 9 ], the architectural complexity increases further [ 7 , 8 , 9 ]. The main trade-off of the greater quantity of hardware components in AICs is the higher level of distortion and noise.…”

Section: Analog-to-information Convertersmentioning

confidence: 99%

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation

Iadarola

Daponte

Vito

et al. 2023

Sensors

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Data acquisition systems have shown the need of wideband spectrum monitoring for many years. This paper describes and discusses a recently proposed architecture aimed at acquiring efficiently wideband signals, named the Analog-to-Information Converter (AIC). AIC framework and working principle implementing the sub-Nyquist sampling are analyzed in general terms. Attention is specifically focused on the idea of exploiting the condition of the signals that, despite their large bandwidth, have a small information content in the frequency domain. However, as clarified in the paper, employing AICs in measurement instrumentation necessarily entails their characterization, through the analysis of their building blocks and the corresponding non-idealities, in order to improve the signal reconstruction.

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Section: Analog-to-information Convertersmentioning

confidence: 99%

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation

Iadarola

Daponte

Vito

et al. 2023

Sensors

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“…2, we use NUWS [24] to acquire CS measurements at each antenna. NUWS efficiently samples analog signals by taking inner products between the analog signal and wavelets that can be generated efficiently in hardware [36]. For antenna b, the NUWS measurement process can be modeled as y .…”

Section: Nonuniform Wavelet Sampling (Nuws)mentioning

confidence: 99%

Sample-Efficient Spatio-Spectral Whitespace Detection Using Least Matching Pursuit

Gönültaş¹,

Soni

Apsel

et al. 2021

IEEE Access

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Multi-antenna wireless communication improves spectral efficiency by reusing frequencies at different locations in space using beamforming and spatial multiplexing. In the past, research has extensively focused on dynamically reusing unused frequency bands to optimize spectrum usage, but methods that identify unused resources in space appear to be unexplored. In this paper, we propose a sample-efficient whitespace detection pipeline for multi-antenna radio-frequency (RF) transceivers that detects unused resources in both frequency and space. Our spatio-spectral whitespace detection pipeline relies on multi-antenna nonuniform wavelet sampling, which identifies unused frequencies in space at sub-Nyquist sampling rates. We demonstrate the efficacy of our approach via system simulations and show that reliable spatio-spectral whitespace detection is possible with 16× lower sampling rates than methods relying on Nyquist sampling.INDEX TERMS Coherence, compressive sensing (CS), least matching pursuit (LMP), multi-antenna communication, nonuniform wavelet sampling (NUWS), spatio-spectral sensing, whitespace detection.

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“…Non-uniform wavelet sampling (NUWS) has been proposed in [16] as a flexible and hardware-friendly compressive sensing strategy for RF sensing [18] and feature extraction tasks [19]. In short, NUWS combines the advantages of nonuniform sampling [9] and random modulation [20].…”

Section: A Non-uniform Wavelet Sampling (Nuws)mentioning

confidence: 99%

“…The tunability of NUWS enables one to adapt the sequence of wavelets to the task at hand. For our RF sensing application, we consider Haar-like wavelets [19] with entries in {+1, 0, −1} that can easily be generated with mixed-signal circuitry [18].…”

Section: A Non-uniform Wavelet Sampling (Nuws)mentioning

confidence: 99%

Identifying Unused RF Channels Using Least Matching Pursuit

Gönültaş

Taghavi

Soni

et al. 2020

2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Cognitive radio aims at identifying unused radiofrequency (RF) bands with the goal of re-using them opportunistically for other services. While compressive sensing (CS) has been used to identify strong signals (or interferers) in the RF spectrum from sub-Nyquist measurements, identifying unused frequencies from CS measurements appears to be uncharted territory. In this paper, we propose a novel method for identifying unused RF bands using an algorithm we call least matching pursuit (LMP). We present a sufficient condition for which LMP is guaranteed to identify unused frequency bands and develop an improved algorithm that is inspired by our theoretical result. We perform simulations for a CS-based RF whitespace detection task in order to demonstrate that LMP is able to outperform black-box approaches that build on deep neural networks.

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Hardware platform of Analog-to-Information converter using Non Uniform Wavelet Bandpass Sampling for RF signal activity detection

Cited by 6 publications

References 11 publications

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation

Over the Limits of Traditional Sampling: Advantages and Issues of AICs for Measurement Instrumentation

Sample-Efficient Spatio-Spectral Whitespace Detection Using Least Matching Pursuit

Identifying Unused RF Channels Using Least Matching Pursuit

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