Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning
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 pot...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/172562 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-172562 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1725622023-12-13T01:46:16Z Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning Xiao, Yong Sun, Zijian Shi, Guangming Niyato, Dusit School of Computer Science and Engineering Engineering::Computer science and engineering Semantic Communication Collaborative Reasoning 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 in 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 play 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 destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple 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 to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions. The work of Yong Xiao was supported in part by the Major Key Project of Peng Cheng Laboratory under Grant PCL2021A12, in part by the National Natural Science Foundation of China under Grant 62071193, and in part by the Key Research and Development Program of Hubei Province of China under Grant 2021EHB015 and Grant 2020BAA002. The work of Guangming Shi was supported in part by the Major Key Project of Peng Cheng Laboratory under Grant PCL2021A12 and in part by the National Natural Science Foundation of China under Grant 61871304 and Grant 61976169. 2023-12-13T01:46:16Z 2023-12-13T01:46:16Z 2023 Journal Article Xiao, Y., Sun, Z., Shi, G. & Niyato, D. (2023). Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning. IEEE Journal On Selected Areas in Communications, 41(3), 639-658. https://dx.doi.org/10.1109/JSAC.2022.3229419 0733-8716 https://hdl.handle.net/10356/172562 10.1109/JSAC.2022.3229419 2-s2.0-85146226550 3 41 639 658 en IEEE Journal on Selected Areas in Communications © 2022 IEEE. All rights reserved. |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering Semantic Communication Collaborative Reasoning |
spellingShingle |
Engineering::Computer science and engineering Semantic Communication Collaborative Reasoning Xiao, Yong Sun, Zijian Shi, Guangming Niyato, Dusit Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
description |
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 in 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 play 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 destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple 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 to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions. |
author2 |
School of Computer Science and Engineering |
author_facet |
School of Computer Science and Engineering Xiao, Yong Sun, Zijian Shi, Guangming Niyato, Dusit |
format |
Article |
author |
Xiao, Yong Sun, Zijian Shi, Guangming Niyato, Dusit |
author_sort |
Xiao, Yong |
title |
Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
title_short |
Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
title_full |
Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
title_fullStr |
Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
title_full_unstemmed |
Imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
title_sort |
imitation learning-based implicit semantic-aware communication networks: multi-layer representation and collaborative reasoning |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/172562 |
_version_ |
1787136508438773760 |