Goal-oriented Semantic Communications for Avatar-centric Augmented Reality
Zhe Wang, Yansha Deng, A. Hamid Aghvami
TL;DR
This work tackles the bandwidth and latency challenges of avatar-centric augmented reality by proposing a GOAL-oriented semantic communication framework (GSAR) that combines semantic extraction with goal-driven transmission and avatar pose recovery. A key contribution is Avatar-based Semantic Ranking (AbSR), which, aided by CSI feedback, maps more important semantic content to more reliable subchannels, while leveraging a shared base knowledge to maintain avatar integrity. The semantic tier is realized via SANet-derived skeleton information, with GSAR variants (GSAR, E-GSAR, EC-GSAR) differing in how semantics and base knowledge are defined and utilized for pose reconstruction. Simulation results show substantial improvements in latency (up to 95.6%), geometry quality (up to 82.4%), and color fidelity (up to 20.4%), validating the potential of GO semantic communications to enhance AR QoE in 6G networks. The framework lays a foundation for future work on CSI estimation, channel adaptation, and broader avatar representations.
Abstract
Upon the advent of the emerging metaverse and its related applications in Augmented Reality (AR), the current bit-oriented network struggles to support real-time changes for the vast amount of associated information, hindering its development. Thus, a critical revolution in the Sixth Generation (6G) networks is envisioned through the joint exploitation of information context and its importance to the task, leading to a communication paradigm shift towards semantic and effectiveness levels. However, current research has not yet proposed any explicit and systematic communication framework for AR applications that incorporate these two levels. To fill this research gap, this paper presents a task-oriented and semantics-aware communication framework for augmented reality (TSAR) to enhance communication efficiency and effectiveness in 6G. Specifically, we first analyse the traditional wireless AR point cloud communication framework and then summarize our proposed semantic information along with the end-to-end wireless communication. We then detail the design blocks of the TSAR framework, covering both semantic and effectiveness levels. Finally, numerous experiments have been conducted to demonstrate that, compared to the traditional point cloud communication framework, our proposed TSAR significantly reduces wireless AR application transmission latency by 95.6%, while improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.