# Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design

arXiv: https://arxiv.org/abs/2212.06463 · HTML: https://mlsanity.com/paper/2212.06463/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [RelTR: Relation Transformer for Scene Graph Generation](https://mlsanity.com/paper/2201.11460/) (2022)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis](https://mlsanity.com/paper/2209.12274/) (2022)
- [Will Metaverse be NextG Internet? Vision, Hype, and Reality](https://mlsanity.com/paper/2201.12894/) (2022)
- [Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach](https://mlsanity.com/paper/2111.10548/) (2021)
- [Compute- and Data-Intensive Networks: The Key to the Metaverse](https://mlsanity.com/paper/2204.02001/) (2022)

## Built on by (most cited first, 4 of 4)

- [Towards Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities](https://mlsanity.com/paper/2307.06687/) (2023)
- [LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication](https://mlsanity.com/paper/2401.02675/) (2024)
- [Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives](https://mlsanity.com/paper/2405.04718/) (2024)
- [Research on environment perception and behavior prediction of intelligent UAV based on semantic communication](https://mlsanity.com/paper/2501.04480/) (2025)

## Abstract (arXiv)

Semantic communication (SemCom) and edge computing are two disruptive solutions to address emerging requirements of huge data communication, bandwidth efficiency and low latency data processing in Metaverse. However, edge computing resources are often provided by computing service providers and thus it is essential to design appealingly incentive mechanisms for the provision of limited resources. Deep learning (DL)- based auction has recently proposed as an incentive mechanism that maximizes the revenue while holding important economic properties, i.e., individual rationality and incentive compatibility. Therefore, in this work, we introduce the design of the DLbased auction for the computing resource allocation in SemComenabled Metaverse. First, we briefly introduce the fundamentals and challenges of Metaverse. Second, we present the preliminaries of SemCom and edge computing. Third, we review various incentive mechanisms for edge computing resource trading. Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse. Simulation results demonstrate that the DL-based auction improves the revenue while nearly satisfying the individual rationality and incentive compatibility constraints.