A Minesweeper Algorithm for Improved Signal Area Estimation in Spectrum Aware Systems

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

Lehtomäki

2022

Self Cite

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: 99%

Section: B Existing Sae Methodsmentioning

confidence: 99%

Section: B Existing Sae Methodsmentioning

confidence: 99%

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

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

Lehtomäki

2022

Self Cite

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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%

“…Some variants to reduce the impact of false alarms are proposed in [12]- [14]. An approach based on mathematical morphology principles is proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Signal Area Estimation Based on Edge Detection and Flood Fill

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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Signal area estimation based on deep learning

2023

Physical Communication