Evaluation of the Impact of Thresholding and Frequency/Time Resolution on Signal Area Estimation Methods

Alammar, Mohammed M.; López-Benítez, Miguel

doi:10.1109/vtc2021-spring51267.2021.9448916

Cited by 5 publications

(9 citation statements)

References 7 publications

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“…The configuration of such threshold as well as the resolution of the spectrogram can have a significant impact on the performance of SAE methods. These aspects are out of the scope of this work but a detailed analysis and discussion can be found in [18]. The examples shown in Figs.…”

Section: Proposed Ht-based Sae Methodsmentioning

confidence: 96%

“…The performance of the methods proposed in this work is evaluated by means of Monte Carlo simulations and hardware experiments. Simulations were performed following the same procedure used in [15], [18]. The simulation procedure utilises multiple random two-dimensional time-frequency binary data grids containing channelised SAs with fixed bandwidth and random durations.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Step 1. Create clean time-frequency test grids: For each simulated SNR, a group of 100 test grids is randomly generated with resolution of 50×100 tiles, which can be considered of medium size according to [18]. The horizontal size (100 tiles) is the number of frequency bins while the vertical size (50 tiles) is given by the time resolution of the spectrum measurements.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…In power spectrograms, each tile represents a continuous-domain power value, which can be mapped to a discrete-domain occupied/empty state by using an appropriate decision threshold [17]. The appropriate configuration of such decision threshold in the context of SAE has been investigated in [18]. This binary time-frequency grid provides the fundamental data required for SAE and is the input information that most SAE methods need, based on which they will attempt to estimate as accurately as possible rectangularly-shaped regions containing adjacent tiles in the occupied state.…”

Section: A Problem Description and Formulationmentioning

confidence: 99%

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Automatic Extraction of Signal Areas from Radio Spectrograms Based on the Hough Transform

Alammar

López-Benítez

Lehtomäki

2022

2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Radio communication signals are often represented in many practical application scenarios as a spectrogram, which indicates the power observed at several discrete time instants and frequency points within a certain time interval and frequency band, respectively. The concept of Signal Area (SA) was recently introduced in the context of spectrum occupancy measurements as the rectangular region in the time-frequency domain where a signal is believed to be present. An accurate estimation of the original SA for each radio transmission contained in a spectrogram can provide valuable information in many practical application scenarios, such as autonomous spectrum-aware wireless communication systems. In this context, this work proposes new methods for an accurate Signal Area Estimation (SAE) based on the application of the Hough Transform (HT) combined with other techniques from the field of image processing. The performance of the proposed methods is evaluated by means of simulations and experiments. The obtained results show that they can achieve a high level of SAE accuracy. Moreover, an interesting and distinguishing feature of the proposed methods is their ability to not only improve the accuracy of the SAE but also to extract automatically the coordinates and dimensions of each SA detected in a radio spectrogram. This feature can be useful in the automatic processing of radio spectrograms, for example in the context of autonomous spectrum-aware wireless systems.

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Section: Proposed Ht-based Sae Methodsmentioning

confidence: 96%

Section: Evaluation Methodologymentioning

confidence: 99%

Section: Evaluation Methodologymentioning

confidence: 99%

Section: A Problem Description and Formulationmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Extraction of Signal Areas from Radio Spectrograms Based on the Hough Transform

Alammar

López-Benítez

Lehtomäki

2022

2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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“…The performance of the SAE strategy explored in this work was evaluated with software simulations and corroborated with hardware experiments based on an tailored experimental prototype specifically built to this end. Monte Carlo simulations were performed following the same procedure used in [15], [42], which is summarised in the following steps:…”

Section: Evaluation Methodologymentioning

confidence: 99%

Enhanced Signal Area Estimation Based on Edge Detection and Flood Fill

Alammar

López-Benítez

2022

IEEE Access

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Spectrograms are a common form of time-frequency representation of wireless communication signals. In many practical scenarios spectrograms need to be processed to identify accurately the time-frequency region occupied by each individual radio transmission, which in this work is referred to as Signal Area (SA). Several methods have been proposed in the literature for Signal Area Estimation (SAE), however their performance degrades significantly in the low SNR regime. In this context, this work proposes a novel approach for SAE based on the use of two well-known techniques from the field of image processing, namely edge detection and flood fill. Edge detection is first employed to identify the edges of potential SAs and flood fill is then used to fill the area inside the estimated edges in order to produce a more accurate estimation of the SAs present in a spectrogram. The performance of three popular edge detection methods (gradient magnitude, Laplacian of Gaussian and Canny) is assessed both with simulations and experimental data. The obtained results show that the proposed strategy can improve significantly the performance of existing SAE methods in the low SNR regime (with estimation accuracy improvements up to 38-45% within the SNR interval from -20 dB to -10 dB) when used as a pre/post-processing stage, thus improving their sensitivity and effectively extending their overall SNR range of operation.INDEX TERMS Signal area estimation, spectrum awareness, image processing, edge detection, flood fill.

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Iterative Pyramidal Filtering Method for Improved Signal Recognition in Radio Spectrograms

López-Benítez

2022

IEEE Wireless Commun. Lett.

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Evaluation of the Impact of Thresholding and Frequency/Time Resolution on Signal Area Estimation Methods

Cited by 5 publications

References 7 publications

Automatic Extraction of Signal Areas from Radio Spectrograms Based on the Hough Transform

Automatic Extraction of Signal Areas from Radio Spectrograms Based on the Hough Transform

Enhanced Signal Area Estimation Based on Edge Detection and Flood Fill

Iterative Pyramidal Filtering Method for Improved Signal Recognition in Radio Spectrograms

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