Performance Analysis of RF Signal Detection and Classification with Spiking Neural Network Using Rate Coding and Temporal Coding 


Vol. 48,  No. 2, pp. 162-171, Feb.  2023
10.7840/kics.2023.48.2.162


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  Abstract

Spiking Neural Networks (SNNs) are the third generation of neural networks and attract many researchers’ attention currently. SNN uses discrete spikes and spiking neurons that deliver information when their membrane potentials reach threshold to process data. Due to this, SNN is more energy efficient than other neural networks and is known to have good performance with processing sequential data. However, SNN has been confined to image analysis like other neural networks and it is not being used in other applications. Therefore, in this paper, we propose to use SNN for detecting and classifying Radio Frequency (RF) signals and measure its performance. Furthermore, we apply rate encoding and temporal encoding for translating RF signals into spikes. We measure performance and computational complexity of each encoding scheme and propose efficient encoding scheme according to Signal-to-Noise Ratio (SNR).

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[IEEE Style]

H. Lee and J. Lim, "Performance Analysis of RF Signal Detection and Classification with Spiking Neural Network Using Rate Coding and Temporal Coding," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 2, pp. 162-171, 2023. DOI: 10.7840/kics.2023.48.2.162.

[ACM Style]

Hyun-Jong Lee and Jae-Han Lim. 2023. Performance Analysis of RF Signal Detection and Classification with Spiking Neural Network Using Rate Coding and Temporal Coding. The Journal of Korean Institute of Communications and Information Sciences, 48, 2, (2023), 162-171. DOI: 10.7840/kics.2023.48.2.162.

[KICS Style]

Hyun-Jong Lee and Jae-Han Lim, "Performance Analysis of RF Signal Detection and Classification with Spiking Neural Network Using Rate Coding and Temporal Coding," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 2, pp. 162-171, 2. 2023. (https://doi.org/10.7840/kics.2023.48.2.162)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880