Toward Wisdom-Evolutionary and Primitive-Concise 6G:A New Paradigm of Semantic Communication Networks

The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G networks.Such hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological innovations.To this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G network.In particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network architecture.In the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information flows.The proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in 6G.It features a lower bandwidth requirement,less redundancy,and more accurate intent identification.We also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.

Wireless semantic communication based on semantic matching multiple access and intent bias multiplexing

This paper proposes a multi-access and multi-user semantic communication scheme based on semantic matching and intent deviation to address the increasing demand for wireless users and data.The scheme enables flexible management of long frames,allowing each unit of bandwidth to support a higher number of users.By leveraging semantic classification,different users can independently access the network through the transmission of long concatenated sequences without modifying the existing wireless communication architecture.To overcome the potential disadvantage of incomplete semantic database matching leading to semantic intent misunderstanding,the scheme proposes using intent deviation as an advantage.This allows different receivers to interpret the same semantic information differently,enabling multiplexing where one piece of information can serve multiple users with distinct purposes.Simulation results show that at a bit error rate(BER)of 0.1,it is possible to reduce the transmission by approximately 20 semantic basic units.

Model division multiple access for semantic communications

In a multi-user system,system resources should be allocated to different users.In traditional communication systems,system resources generally include time,frequency,space,and power,so multiple access technologies such as time division multiple access(TDMA),frequency division multiple access(FDMA),space division multiple access(SDMA),code division multiple access(CDMA),and non-orthogonal multiple access(NOMA)are widely used.In semantic communication,which is considered a new paradigm of the next-generation communication system,we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features.From the high-dimensional semantic space,we excavate the shared and personalized information of semantic information and propose a novel multiple access technology,named model division multiple access(MDMA),which is based on the resource of the semantic domain.From the perspective of information theory,we prove that MDMA can attain more performance gains than traditional multiple access technologies.Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies,and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise(AWGN)channel under the low signal-to-noise(SNR)condition.

Fundamental Limitation of Semantic Communications:Neural Estimation for Rate-Distortion

This paper studies the fundamental limit of semantic communications over the discrete memoryless channel.We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state,both of which are recovered at the receiver.To derive the performance limitation,we adopt the semantic rate-distortion function(SRDF)to study the relationship among the minimum compression rate,observation distortion,semantic distortion,and channel capacity.For the case with unknown semantic source distribution,while only a set of the source samples is available,we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution.Furthermore,for a special case where the semantic state is a deterministic function of the observation,we design a cascade neural network to estimate the SRDF.For the case with perfectly known semantic source distribution,we propose a general Blahut-Arimoto(BA)algorithm to effectively compute the SRDE.Finally,experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.

Intellicise communication system: model-driven semantic communications

As one of the critical technologies for the 6 th generation mobile communication system(6 G) mobile communication systems, artificial intelligence(AI) technology will provide complete automation for connecting the virtual and physical worlds. In order to construct the future ubiquitous intelligent network, people are beginning to rethink how mobile communication systems transmit and exploit intelligent information. This paper proposes a new communication paradigm, called the Intellicise communication system: model-driven semantic communication. Intellicise communication system is built on top of the traditional communication system and innovatively adds a new feature dimension on top of the traditional source coding, which enables the communication system to evolve from the traditional transmission of bit to the transmission of "model". Like the semantic base(Seb) for semantic communication, the model is considered as the new feature obtained from the joint source-channel coding. The sink node can re-construct the original signal based on the received model and the encoded sequence. In addition, the performance evaluation metrics and the implementation details of the Intellicise communication system are discussed in this paper. Finally, preliminary results of model-driven image transmission in the Intellicise communication system are presented.

What Is Semantic Communication?A View on Conveying Meaning in the Era of Machine Intelligence

In the 1940s,Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel.Guided by this fundamental work,the main theme of wireless system design up until the fifth generation(5G)was the data rate maximization.In Shannon’s theory,the semantic aspect and meaning of messages were treated as largely irrelevant to communication.The classic theory started to reveal its limitations in the modern era of machine intelligence,consisting of the synergy between Internet-of-things(IoT)and artificial intelligence(AI).By broadening the scope of the classic communication-theoretic framework,in this article,we present a view of semantic communication(SemCom)and conveying meaning through the communication systems.We address three communication modalities:human-to-human(H2H),human-to-machine(H2M),and machine-to-machine(M2M)communications.The latter two represent the paradigm shift in communication and computing,and define the main theme of this article.H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and“dialogue”.On the other hand,M2M SemCom refers to effective techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network.The first part of this article focuses on introducing the SemCom principles including encoding,layered system architecture,and two design approaches:1)layer-coupling design;and 2)end-to-end design using a neural network.The second part focuses on the discussion of specific techniques for different application areas of H2M SemCom[including human and AI symbiosis,recommendation,human sensing and care,and virtual reality(VR)/augmented reality(AR)]and M2M SemCom(including distributed learning,split inference,distributed consensus,and machine-vision cameras).Finally,we discuss the approach for designing SemCom systems based on knowledge graphs.We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation(6G)featuring connected intelligence and integrated sensing,computing,communication,and control.

Multi-User Semantic Communications System with Spectrum Scarcity

Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing on spectrum scarcity,expected to afflict the upcoming sixth generation(6G)networks,this paper analyses the semantic communications behavior in the context of a cell-dense scenario,in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability.In such a context,artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm.As a consequence,a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework.Finally,extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.

Deep Learning-Based Semantic Feature Extraction:A Literature Review and Future Directions

Semantic communication,as a critical component of artificial intelligence(AI),has gained increasing attention in recent years due to its significant impact on various fields.In this paper,we focus on the applications of semantic feature extraction,a key step in the semantic communication,in several areas of artificial intelligence,including natural language processing,medical imaging,remote sensing,autonomous driving,and other image-related applications.Specifically,we discuss how semantic feature extraction can enhance the accuracy and efficiency of natural language processing tasks,such as text classification,sentiment analysis,and topic modeling.In the medical imaging field,we explore how semantic feature extraction can be used for disease diagnosis,drug development,and treatment planning.In addition,we investigate the applications of semantic feature extraction in remote sensing and autonomous driving,where it can facilitate object detection,scene understanding,and other tasks.By providing an overview of the applications of semantic feature extraction in various fields,this paper aims to provide insights into the potential of this technology to advance the development of artificial intelligence.

SST-V:A Scalable Semantic Transmission Framework for Video

The emerging new services in the sixth generation(6G)communication system impose increasingly stringent requirements and challenges on video transmission.Semantic communications are envisioned as a promising solution to these challenges.This paper provides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm,named scalable semantic transmission framework for video(SST-V),which jointly considers the semantic importance and channel conditions.Specifically,a semantic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level,facilitating high-efficiency semantic coding.By further considering the channel condition,a cascaded learning based scalable joint semanticchannel coding algorithm is proposed,which autonomously adapts the semantic coding and channel coding strategies to the specific signalto-noise ratio(SNR).Simulation results show that SST-V achieves better video reconstruction performance,while significantly reducing the transmission overhead.

Edge Semantic Cognitive Intelligence for 6G Networks:Novel Theoretical Models,Enabling Framework,and Typical Applications

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks,but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated.To this end,this article discusses the concepts of edge intelligence from the semantic cognitive perspective.Two instructive theoretical models for edge semantic cognitive intelligence(ESCI)are first established.Afterwards,the ESCI framework orchestrating deep learning with semantic communication is discussed.Two representative applications are present to shed light on the prospect of ESCI in 6G networks.Some open problems are finally listed to elicit the future research directions of ESCI.

Semantic information processing for interoperability in the Industrial Internet of Things

With the advent of the Internet of Everything(IoE),the concept of fully interconnected systems has become a reality,and the need for seamless communication and interoperability among different industrial systems has become more pressing than ever before.To address the challenges posed by massive data traffic,we demonstrate the potentials of semantic information processing in industrial manufacturing processes and then propose a brief framework of semantic processing and communication system for industrial network.In particular,the scheme is featured with task-orientation and collaborative processing.To illustrate its applicability,we provide examples of time series and images,as typical industrial data sources,for practical tasks,such as lifecycle estimation and surface defect detection.Simulation results show that semantic information processing achieves a more efficient way of information processing and exchanging,compared to conventional methods,which is crucial for handling the demands of future interconnected industrial networks.

SNR-adaptive deep joint source-channel coding scheme for image semantic transmission with convolutional block attention module

With the development of deep learning(DL),joint source-channel coding(JSCC)solutions for end-to-end transmission have gained a lot of attention.Adaptive deep JSCC schemes support dynamically adjusting the rate according to different channel conditions during transmission,enhancing robustness in dynamic wireless environment.However,most of the existing adaptive JSCC schemes only consider different channel conditions,ignoring the different feature importance in the image processing and transmission.The uniform compression of different features in the image may result in the compromise of critical image details,particularly in low signal-to-noise ratio(SNR)scenarios.To address the above issues,in this paper,a dual attention mechanism is introduced and an SNR-adaptive deep JSCC mechanism with a convolutional block attention module(CBAM)is proposed,in which matrix operations are applied to features in spatial and channel dimensions respectively.The proposed solution concatenates the pooling feature with the SNR level and passes it sequentially through the channel attention network and spatial attention network to obtain the importance evaluation result.Experiments show that the proposed solution outperforms other baseline schemes in terms of peak SNR(PSNR)and structural similarity(SSIM),particularly in low SNR scenarios or when dealing with complex image content.

Federated Learning for 6G:Applications,Challenges,and Opportunities

Standard machine-learning approaches involve the centralization of training data in a data center,where centralized machine-learning algorithms can be applied for data analysis and inference.However,due to privacy restrictions and limited communication resources in wireless networks,it is often undesirable or impractical for the devices to transmit data to parameter sever.One approach to mitigate these problems is federated learning(FL),which enables the devices to train a common machine learning model without data sharing and transmission.This paper provides a comprehensive overview of FL applications for envisioned sixth generation(6G)wireless networks.In particular,the essential requirements for applying FL to wireless communications are first described.Then potential FL applications in wireless communications are detailed.The main problems and challenges associated with such applications are discussed.Finally,a comprehensive FL implementation for wireless communications is described.

Semantic information processing in industrial networks

The industrial Internet of things(industrial IoT, IIoT) aims at connecting everything, which poses severe challenges to existing wireless communication. To handle the demand for massive access in future industrial networks, semantic information processing is integrated into communication systems so as to improve the effectiveness and efficiency of data transmission. The semantic paradigm is particularly suitable for the purpose-oriented information exchanging scheme in industrial networks. To illustrate its applicability, typical industrial data are investigated, i.e., time series and images. Simulation results demonstrate the superiority of semantic information processing, which achieves a better rate-utility tradeoff than conventional signal processing.