Semantic Communication Research Articles

Mechanism Design for Semantic Communication in UAV-Assisted Metaverse: A Combinatorial Auction Approach

Mechanism Design for Semantic Communication in UAV-Assisted Metaverse: A Combinatorial Auction Approach

Preface

The International Conference on Machine Learning and Automation (CONF-MLA) is an annual conference focusing on research areas including the development of engineering and machine learning applications. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advances and developments in relevant fields.This volume contains the papers of the 2023 International Conference on Machine Learning and Automation (CONF-MLA 2023). Each of these papers has gained a comprehensive review by the editorial team and professional reviewers. Each paper has been examined and evaluated for its theme, structure, method, content, language, and format.Cooperating with prestigious universities, CONF-MLA 2023 organized five workshops in Chicago, Auckland, Oxford, Glasgow, and Adana. Dr. Marwan Omar chaired the workshop “The Role of Machine Learning in Advancing Cyber Security”, which was held at the Illinois Institute of Technology. Dr. Alan Wang chaired the workshop “Workshop on Machine Learning for Medicine 2023 (WMLM2023)” at the University of Auckland. Prof. Hong Zhu chaired the workshop “Automation of Testing Machine Learning” at Oxford Brookes University. Prof. Anil Fernando chaired the workshop “Autoencoder Based Semantic Communications for Image Transmission on Error Prone Channels” at University of Strathclyde. Dr. Mustafa ISTANBULLU chaired the workshop “Process Design of Semiconductor Device Fabrication: From Concept to Production” at Çukurova University.Besides these workshops, CONF-MLA 2023 also held an online session. Eminent professors from top universities worldwide were invited to deliver keynote speeches in this online session, including Dr. Abdullahi Arabo from University of the West of England, Dr. Roman Bauer from University of Surrey, Dr. Ali Darejeh from UNSW Sydney, etc. They have given keynote speeches on related topics of machine learning, software engineering, automation, and other engineering areas.On behalf of the committee, we would like to give sincere gratitude to all authors and speakers who have made their contributions to CONF-MLA 2023, editors and reviewers who have guaranteed the quality of papers with their expertise, and the committee members who have devoted themselves to the success of CONF-MLA 2023.Dr. Marwan OmarGeneral Chair of Conference CommitteeList of Committee Members CONF-MLA 2023 is available in this pdf.

Joint User Association and Bandwidth Allocation in Semantic Communication Networks

Joint User Association and Bandwidth Allocation in Semantic Communication Networks

Adaptive Modulation and Retransmission Scheme for Semantic Communication Systems

Adaptive Modulation and Retransmission Scheme for Semantic Communication Systems

Semantic Communication: A Survey of Its Theoretical Development.

In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.

Joint Task and Data-Oriented Semantic Communications: A Deep Separate Source-Channel Coding Scheme

Joint Task and Data-Oriented Semantic Communications: A Deep Separate Source-Channel Coding Scheme

ChatGPT and the entangled evolution of society, education, and technology: A systems theory perspective

This paper presents a novel contribution to the discourse surrounding Large Language Models (LLMs) like ChatGPT in relation to education and society by using systems theory. We argue that ChatGPT can be understood not just as an ‘artificial’ intelligence but that it is entangled in the evolution of society and therefore education. ChatGPT is a subsystem of the autopoietic system of technology, which in modern society mediates between individual thinking, the physical world, and between thought and society. It is an instrumental tool and a semantic communication medium. With this bimodal framing, we consider ChatGPT and its role in society and education and consider the uses and implications of the technology. In this we respond to the need to introduce a scientific understanding of ChatGPT. We consider its emerging role in promoting educational inclusion, while also reflecting on challenges and limitations. We conclude by identifying the critical multi-dimensional skill sets required for individuals in a ChatGPT-integrated society and calls for strategic educational policies to facilitate this integration responsibly. Overall, this study paves the way for further research by providing a foundational understanding of LLMs through systems theory, thereby informing their ethical and effective incorporation into education.

Design and experimental demonstration of underwater wireless optical communication system based on semantic communication paradigm.

Underwater wireless optical communication (UWOC) has been widely studied as a key technology for ocean exploration and exploitation. However, current UWOC systems neglect semantic information of transmitted symbols, leading to unnecessary consumption of communication resources for transmitting non-essential data. In this paper, we propose and demonstrate a deep-learning-based underwater wireless optical semantic communication (UWOSC) system for image transmission. By utilizing a deep residual convolutional neural network, the semantic information can be extracted and mapped into the transmitted symbols. Moreover, we design a channel model based on long short-term memory network and employ a two-phase training strategy to ensure that the system matches the underwater channel. To evaluate the performance of the proposed UWOSC system, we conduct a series of experiments on an emulated UWOC experimental platform, in which the effects of different turbidity channel environments and bandwidth compression ratios are investigated. Experimental results show that the UWOSC system exhibits superior performance compared to the conventional communication schemes, particularly in challenging channel environments and low bandwidth compression ratios.

Timely Inference over Networks

Brief Biography: Md Kamran Chowdhury Shisher is a final-year Ph.D. candidate in the Department of Electrical and Computer Engineering at Auburn University, Auburn, AL, USA. He expects to graduate by May 2024. His Ph.D. works are advised by Yin Sun. Shisher's research interests include Information Freshness, Wireless Networks, Semantic Communications, and Machine Learning. His Ph.D. works focus on designing low-complexity optimal scheduling algorithms for remote inference systems. He also developed an information-theoretic tool to analyze the impact of freshness on the performance of real-time applications. He received his M.Sc. degree in Electrical Engineering at Auburn University in 2022. He completed his B.Sc. in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology in 2017.

UDSem: A Unified Distributed Learning Framework for Semantic Communications Over Wireless Networks

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost the communication efficiency by only transmitting the semantics of data. However, ESC is data-hungry for training millions of neural parameters, while uploading massive data generated by users/devices to a central server is usually impractical due to privacy issues, regulatory guidelines and network congestion, etc. Inspired by federated learning, where each device contributes to the parameters' update by independently computing the gradient based on its local data, we present UDSem, a unified distributed learning framework of semantic communications for texts and images over wireless networks. The key ingredients of our UDSem include 1) a flexible learning mechanism that can be tailored to a device based on its computing capabilities, 2) an efficient learning approach that splits the system into multiple modules and updates the parameters independently both on clients and the central server, 3) a mixed aggregation method that globally updates the neural parameters for each module of the ESC system. Equipped with these ingredients, our proposed UDSem is able to properly fill the gap between the training efficiency of ESC systems and the privacy requirement of users' data. Experimental results over two public benchmarks show the superiority of our UDSem in terms of convergence speed and semantic interpretation, potentially paving the way for distributed semantic communications in future wireless networks.

A Semantic Communication and VVC Based Hybrid Video Coding System

A Semantic Communication and VVC Based Hybrid Video Coding System

Joint Edge Computing and Semantic Communication in UAV-Enabled Networks

Joint Edge Computing and Semantic Communication in UAV-Enabled Networks

A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication

A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication

Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges

Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges

Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing

Will 6G be Semantic Communications? Opportunities and Challenges from Task Oriented and Secure Communications to Integrated Sensing

Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey

Resource Management, Security, and Privacy Issues in Semantic Communications: A Survey

Image Semantic Steganography: A Way to Hide Information in Semantic Communication

Image Semantic Steganography: A Way to Hide Information in Semantic Communication

Distortion Resilience for Goal-Oriented Semantic Communication

Distortion Resilience for Goal-Oriented Semantic Communication

Deep Learning Based Superposition Coded Modulation for Hierarchical Semantic Communications over Broadcast Channels

Deep Learning Based Superposition Coded Modulation for Hierarchical Semantic Communications over Broadcast Channels

Engineering a Lightweight Deep Joint Source-Channel Coding Based Semantic Communication System

Engineering a Lightweight Deep Joint Source-Channel Coding Based Semantic Communication System