# Economics of Semantic Communication System: An Auction Approach

arXiv: https://arxiv.org/abs/2208.05040 · HTML: https://mlsanity.com/paper/2208.05040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Deep Learning-Based Communication Over the Air](https://mlsanity.com/paper/1707.03384/) (2017)
- [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [Plug and Play Autoencoders for Conditional Text Generation](https://mlsanity.com/paper/2010.02983/) (2020)
- [Semantic Communication Meets Edge Intelligence](https://mlsanity.com/paper/2202.06471/) (2022)
- [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020)
- [Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication](https://mlsanity.com/paper/2105.10716/) (2021)
- [Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning](https://mlsanity.com/paper/2012.01991/) (2020)
- [Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach](https://mlsanity.com/paper/1711.02844/) (2017)
- [Semantic Communications With AI Tasks](https://mlsanity.com/paper/2109.14170/) (2021)
- [Economics of Semantic Communication System in Wireless Powered Internet of Things](https://mlsanity.com/paper/2110.01423/) (2021)
- [Online Resource Procurement and Allocation in a Hybrid Edge-Cloud Computing System](https://mlsanity.com/paper/2001.08956/) (2020)

## Built on by (most cited first, 2 of 2)

- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication](https://mlsanity.com/paper/2401.02675/) (2024)

## Abstract (arXiv)

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method.