  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.
