Comparative Analysis of Sub-band Allocation Algorithms in In-body Sub-networks Supporting XR Applications
<|reference_start|>Comparative Analysis of Sub-band Allocation Algorithms in In-body Sub-networks Supporting XR Applications: 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.<|reference_end|>
@article{bagherinejad2024comparative,
    title={Comparative Analysis of Sub-band Allocation Algorithms in In-body
  Sub-networks Supporting XR Applications},
    author={Saeed Bagherinejad, Thomas Jacobsen, Nuno K. Pratas, Ramoni O. Adeogun},
    journal={arXiv preprint arXiv:2403.11891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11891},
    primaryClass={cs.IT eess.SP math.IT}
}