Spiking Neural Network-Based Backoff Scheme for Slotted-ALOHA Systems 


Vol. 49,  No. 2, pp. 199-202, Feb.  2024
10.7840/kics.2024.49.2.199


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  Abstract

In this letter, we propose a spiking neural network (SNN)-based backoff scheme to enhance the performance of slotted-ALOHA systems, which operates with relatively low power and implementation complexity. In our scheme, an SNN model takes real-time input of observation data, such as transmission success and failure, from the system operating with binary exponential backoff (BEB), and returns the optimal contention window size. Through simulations, we show that our proposed scheme achieves higher performance compared to the traditional BEB, approaching the performance of the optimal backoff scheme numerically optimized.

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[IEEE Style]

M. J. Kang, J. H. Lee, D. G. Jeong, "Spiking Neural Network-Based Backoff Scheme for Slotted-ALOHA Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 199-202, 2024. DOI: 10.7840/kics.2024.49.2.199.

[ACM Style]

Min Jeong Kang, Jung Hoon Lee, and Dong Geun Jeong. 2024. Spiking Neural Network-Based Backoff Scheme for Slotted-ALOHA Systems. The Journal of Korean Institute of Communications and Information Sciences, 49, 2, (2024), 199-202. DOI: 10.7840/kics.2024.49.2.199.

[KICS Style]

Min Jeong Kang, Jung Hoon Lee, Dong Geun Jeong, "Spiking Neural Network-Based Backoff Scheme for Slotted-ALOHA Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 2, pp. 199-202, 2. 2024. (https://doi.org/10.7840/kics.2024.49.2.199)
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