# Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning

arXiv: https://arxiv.org/abs/2210.16118 · HTML: https://mlsanity.com/paper/2210.16118/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems](https://mlsanity.com/paper/2012.15405/) (2020)
- [ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation](https://mlsanity.com/paper/2105.02254/) (2021)
- [Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data](https://mlsanity.com/paper/2205.00271/) (2022)
- [Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information](https://mlsanity.com/paper/1805.11360/) (2018)
- [TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency](https://mlsanity.com/paper/1611.01702/) (2016)
- [Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet](https://mlsanity.com/paper/2001.10199/) (2020)
- [6G Network AI Architecture for Everyone-Centric Customized Services](https://mlsanity.com/paper/2205.09944/) (2022)
- [Rate-Distortion Theory for Strategic Semantic Communication](https://mlsanity.com/paper/2202.03711/) (2022)
- [Reasoning on the Air: An Implicit Semantic Communication Architecture](https://mlsanity.com/paper/2202.01950/) (2022)
- [A Mathematical Introduction to Generative Adversarial Nets (GAN)](https://mlsanity.com/paper/2009.00169/) (2020)
- [Dynamic Network Slicing for Scalable Fog Computing Systems with Energy Harvesting](https://mlsanity.com/paper/2001.10300/) (2020)
- [AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems](https://mlsanity.com/paper/2106.15477/) (2021)
- [Distributed Resource Allocation for Network Slicing over Licensed and Unlicensed Bands](https://mlsanity.com/paper/2001.10296/) (2020)

## Built on by (most cited first, 16 of 16)

- [Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs](https://mlsanity.com/paper/2409.17557/) (2024)
- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023)
- [Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework](https://mlsanity.com/paper/2306.11229/) (2023)
- [Time-sensitive Learning for Heterogeneous Federated Edge Intelligence](https://mlsanity.com/paper/2301.10977/) (2023)
- [Reasoning with the Theory of Mind for Pragmatic Semantic Communication](https://mlsanity.com/paper/2311.18224/) (2023)
- [Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach](https://mlsanity.com/paper/2302.00207/) (2023)
- [Towards Net-Zero Carbon Emissions in Network AI for 6G and Beyond](https://mlsanity.com/paper/2401.01007/) (2023)
- [Rate-Distortion-Perception Theory for Semantic Communication](https://mlsanity.com/paper/2312.05437/) (2023)
- [Distributed Information Bottleneck Theory for Multi-Modal Task-Aware Semantic Communication](https://mlsanity.com/paper/2510.04000/) (2025)
- [On the Rate-Distortion-Complexity Tradeoff for Semantic Communication](https://mlsanity.com/paper/2602.14481/) (2026)
- [Adversarial Learning for Implicit Semantic-Aware Communications](https://mlsanity.com/paper/2301.11589/) (2023)
- [Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing](https://mlsanity.com/paper/2312.05043/) (2023)
- [Interplay of Semantic Communication and Knowledge Learning](https://mlsanity.com/paper/2402.03339/) (2024)
- [Way to Build Native AI-driven 6G Air Interface: Principles, Roadmap, and Outlook](https://mlsanity.com/paper/2508.15277/) (2025)
- [Implicit Semantic-Aware Communication Based on Hypergraph Reasoning](https://mlsanity.com/paper/2606.20162/) (2026)

## Abstract (arXiv)

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.