Emergent communication for AR

Mobile augmented reality (MAR) is widely acknowledged as one of the ubiquitous interfaces to the digital twin and Metaverse, demanding unparalleled levels of latency, computational power, and energy efficiency. The existing solutions for realizing MAR combine multiple technologies like edge, cloud computing, and fifth-generation (5G) networks. However, the inherent communication latency of visual data imposes apparent limitations on the quality of experience (QoE). To address the challenge, we propose an emergent semantic communication framework to learn the communication protocols in MAR. Specifically, we train two agents through a modified Lewis signaling game to emerge a discrete communication protocol spontaneously. Based on this protocol, two agents can communicate about the abstract idea of visual data through messages with extremely small data sizes in a noisy channel, which leads to message errors. To better simulate real-world scenarios, we incorporate channel uncertainty into our training process. Experiments have shown that the proposed scheme has better generalization on unseen objects than traditional object recognition used in MAR and can effectively enhance communication efficiency through the utilization of small-size messages.

Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing

Mobile augmented reality (MAR) blends a real scenario with overlaid virtual content, which has been envisioned as one of the ubiquitous interfaces to the Metaverse. Due to the limited computing power and battery life of MAR devices, it is common to offload the computation tasks to edge or cloud servers in close proximity. However, existing offloading solutions developed for MAR tasks suffer from high migration overhead, poor scalability, and short-sightedness when applied in provisioning multi-user MAR services. To address these issues, a MAR service-oriented task offloading scheme is designed and evaluated in edge-cloud computing networks. Specifically, the task interdependency of MAR applications is firstly analyzed and modeled by using directed acyclic graphs. Then, we propose a look-ahead offloading scheme based on a modified Monte Carlo tree (MMCT) search, which can run several multi-step executions in advance to get an estimate of the long-term effect of immediate action. Experiment results show that the proposed offloading scheme can effectively improve the quality of service (QoS) in provisioning multi-user MAR services, compared to four benchmark schemes. Furthermore, it is also shown that the proposed solution is stable and suitable for applications in a highly volatile environment.