Semantic communication has recently attracted significant interest from both
industry and academia due to its potential to transform the existing
data-focused communication architecture towards a more generally intelligent
and goal-oriented semantic-aware networking system. Despite its promising
potential, semantic communications and semantic-aware networking are still at
their infancy. Most existing works focus on transporting and delivering the
explicit semantic information, e.g., labels or features of objects, that can be
directly identified from the source signal. The original definition of
semantics as well as recent results in cognitive neuroscience suggest that it
is the implicit semantic information, in particular the hidden relations
connecting different concepts and feature items that plays the fundamental role
in recognizing, communicating, and delivering the real semantic meanings of
messages. Motivated by this observation, we propose a novel reasoning-based
implicit semantic-aware communication network architecture that allows multiple
tiers of CDC and edge servers to collaborate and support efficient semantic
encoding, decoding, and interpretation for end-users. We introduce a new
multi-layer representation of semantic information taking into consideration
both the hierarchical structure of implicit semantics as well as the
personalized inference preference of individual users. We model the semantic
reasoning process as a reinforcement learning process and then propose an
imitation-based semantic reasoning mechanism learning (iRML) solution for the
edge servers to leaning a reasoning policy that imitates the inference behavior
of the source user. A federated GCN-based collaborative reasoning solution is
proposed to allow multiple edge servers to jointly construct a shared semantic
interpretation model based on decentralized knowledge datasets.

本文提出了一种基于推理的隐含语义感知通信网络架构，允许多个 CDC 和边缘服务器协作支持端到端的语义编码，解码和解释，建立多层次表示和个人化推理优先权，并采用强化学习和模仿学习算法进行协作推理。