Comparative Analysis of Sub-band Allocation Algorithms in In-body Sub-networks Supporting XR Applications
Abstract
In-body subnetworks (IBS) are envisioned to support reliable wireless connectivity for emerging applications including extended reality (XR) in the human body. As the deployment of in-body sub-networks is uncontrollable by nature, the dynamic radio resource allocation scheme in place becomes of the uttermost importance for the performance of the in-body sub-networks. This paper provides a comparative study on the performance of the state-of-the-art interference-aware sub-band allocation algorithms in in-body sub-networks supporting the XR applications. The study identified suitable models for characterizing in-body sub-networks which are used in a snapshot-based simulation framework to perform a comprehensive evaluation of the performance of state-of-art sub-band allocation algorithms, including greedy selection, sequential greedy selection (SG), centralized graph coloring (CGC), and sequential iterative sub-band allocation (SISA). The study shows that for XR requirements, the SISA and SG algorithms can support IBS densities up to 75% higher than CGC.
Keywords
Cite
@article{arxiv.2403.11891,
title = {Comparative Analysis of Sub-band Allocation Algorithms in In-body Sub-networks Supporting XR Applications},
author = {Saeed Bagherinejad and Thomas Jacobsen and Nuno K. Pratas and Ramoni O. Adeogun},
journal= {arXiv preprint arXiv:2403.11891},
year = {2024}
}
Comments
Accepted for IEEE WCNC 2024