Rank-Aware Link Adaptation for XR Tethering Groups with Realistic Tethering Link: A Multi-Offset OLLA Framework
Muhammad Ahsen, Boyan Yanakiev, Claudio Rosa, and Ramoni Adeogun

TL;DR
This paper proposes a multi-offset link adaptation framework for higher-rank XR tethering groups, improving throughput prediction and capacity gains under realistic tethering link constraints.
Contribution
It introduces a rank-dependent SINR correction in link adaptation and incorporates a WiFi-based delay model for realistic tethering scenarios.
Findings
MO-OLLA improves throughput prediction accuracy by up to 20%.
Tethering groups achieve 180-200% capacity gains over single-link XR.
Performance gains remain significant under realistic tethering delays.
Abstract
We investigate higher-rank transmissions for multi-connected Extended Reality (XR) devices enabled through tethering group (TGr), in which a nearby tethering User Equipment (UE) cooperates with an XR UE via a short-range tethering link (TL). In contrast to prior studies that are limited to rank-1 transmission and ideal tethering assumptions, we analyze TGr performance under higher-rank point-to-multipoint (PTM) transmission and realistic TL delays. Conventional single Outer Loop Link Adaptation (OLLA) offset results in inaccurate throughput prediction across ranks, leading to suboptimal rank selection. To address this limitation, we propose a multi-offset Outer Loop Link Adaptation (MO-OLLA) framework that introduces rank-dependent signal-to-interference-plus-noise ratio (SINR) correction to improve Link Adaptation (LA) accuracy. Furthermore, a Wireless Fidelity (WiFi) based delay model…
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