  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.
