Comparative Analysis of Sub-band Allocation Algorithms in In-body Sub-networks Supporting XR Applications

TL;DR

This paper addresses the reliability challenges of delivering XR video frames over in-body sub-networks (IBS) by evaluating interference-aware sub-band allocation algorithms. It builds a snapshot-based framework that models IBS deployments with a Thomas Cluster Process, XR DL traffic, and a frame-structured transmission model, and then compares centralized (CGC, SISA) and distributed (Greedy, SG) schemes under realistic channel and signaling constraints. The study finds that the Sequential Iterative Sub-band Allocation (SISA) and Sequential Greedy (SG) algorithms provide the strongest packet delivery performance, enabling higher IBS densities before reliability degrades, while CGC struggles in dense deployments due to signaling overhead and sub-optimal interference management; SISA, though effective, incurs higher signaling costs. Overall, the work highlights important trade-offs between interference mitigation, signaling overhead, and scalability for XR-enabled ultra-dense IBS deployments, pointing to the need for further optimization of resource allocation in 6G in-X sub-networks.

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.

Figures (5)

• Figure 1: Deployment and shape model of $N$ In-Body sub-networks.
• Figure 2: Frame structure and data traffic model for $T_\nu=3T_f$.
• Figure 3: CDF of SINR, for $N=60$, $K=10$, $T_f=5,10$ms.
• Figure 4: Packet Error Rate (PER) versus the Number of IBSs ($N$), for $K=10$ and $T_f=5\text{ms}, 10$ms.
• Figure 5: Packet Error Rate (PER) versus the Number of Sub-bands ($K$), for $N=60$ and $T_f=5$ms.