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Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals

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  • María E. Sousa-Vieira

    (atlanTTic Research Center, Universidade de Vigo, 36310 Vigo, Spain)

  • Manuel Fernández-Veiga

    (atlanTTic Research Center, Universidade de Vigo, 36310 Vigo, Spain)

Abstract

In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/ ∞ process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance.

Suggested Citation

  • María E. Sousa-Vieira & Manuel Fernández-Veiga, 2023. "Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals," Future Internet, MDPI, vol. 15(4), pages 1-18, March.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:4:p:132-:d:1111713
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    References listed on IDEAS

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    1. Pipiras,Vladas & Taqqu,Murad S., 2017. "Long-Range Dependence and Self-Similarity," Cambridge Books, Cambridge University Press, number 9781107039469.
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