Foundations of Semantic Communication Networks (eBook)

Walid Saad, Christina Chaccour, Christo Kurisummoottil Thomas, Merouane Debbah (Herausgeber)

eBook Download: EPUB

2024
650 Seiten
Wiley-IEEE Press (Verlag)
978-1-394-24789-9 (ISBN)

Infos

Comprehensive overview of the principles, theories, and techniques needed to build end-to-end semantic communication systems, with case studies included.

In this rapidly evolving landscape, the integration of connected intelligence applications highlights the pressing need for networks to gain intelligence in a non-siloed and ad hoc manner. The traditional incremental approach to network design is no longer sufficient to support the diverse and dynamic requirements of these emerging applications. This necessitates a paradigm shift towards more intelligent and adaptive network architectures.

From theory to application, Foundations of Semantic Communication Networks describes and provides a comprehensive understanding of everything needed to build end-to-end semantic communication systems. This book covers various interdisciplinary topics such as the mathematical foundations of semantic communications, information theoretical perspectives, joint-source channel coding, semantic-aware resource management strategies, interoperability under heterogeneous semantic communication users, advanced artificial intelligence (AI) and machine reasoning techniques for enabling connected intelligent applications, secure and privacy-preserving semantic communication systems, and the coexistence and interoperability of semantic, goal-oriented, and legacy systems.

The book examines unique features of end-to-end networking with semantic communications, including instilling reasoning behaviors in communication nodes, the role of the semantic plane in information filtering, control of communication and computing resources, transmit and receive signaling schemes, and connected intelligence device control. It emphasizes the importance of data semantics and age of information metrics. The book also discusses the profound impact of semantic communications on the telecom industry, highlighting changes in network performance, resource management, traffic, as well as spectral and energy efficiency.

Furthermore, the book provides insights into the mathematical constructs and AI theories for formulating semantic information, such as topology and category theory. It explores real-world applications, case studies, and future research directions as wireless technologies transition to 6G and beyond.

Written by four recognized experts in the field with a wealth of expertise from academia, industry, and research institutions, Foundations of Semantic Communication Networks addresses sample topics, including:

• Novel Semantic Information Formulations: Proposing new formulations using rigorous mathematical frameworks such as category theory and algebraic topology.
• Practical Applications and Networking Features: Focusing on real-world scenarios, addressing multiple access and networking challenges through collaborative frameworks for multi-modal transmissions, examining multiple access schemes to enhance transmission efficiency, and ensuring coexistence with legacy systems.
• AI-Native Air Interface and Semantic-Aware Resource Allocation: Enabling efficient large-scale systems for 6G and beyond wireless systems through AI-native air interfaces and semantic-aware resource allocation strategies.
• Advanced AI and Machine Reasoning: Utilizing causality and neuro-symbolic artificial intelligence for minimalistic transmissions, and achieving generalizability and transferability across contexts and data distributions to develop high-fidelity semantic communication systems.
• Multi-Domain Security Vulnerabilities: Examining security vulnerabilities associated with deep neural networks in semantic communications, and proposing encrypted, privacy-preserving semantic communication systems (ESCS) as a solution.

Foundations of Semantic Communication Networks is an excellent forward-thinking resource on the subject for readers with a strong background in the subject matter, including graduate-level students, academics, practitioners, and industry researchers.

Walid Saad is a Professor with the Department of Electrical and Computer Engineering, Virginia Tech, USA, where he leads the Network Science, Wireless, and Security (NEWS) Laboratory.

Christina Chaccour is a Network Solutions Manager at Ericsson Inc., USA, where she spearheads product solutions for 5G-Advanced, 6G, and AI integration across North America.

Christo Kurisummoottil Thomas is a Post-Doctoral Fellow with the Department of Electrical and Computer Engineering, Virginia Tech, USA.

Merouane Debbah is a Professor at Khalifa University of Science and Technology, UAE, and founding Director of the KU 6G Research Center.

Textauszug

1
Introduction to Semantic Communications

Christina Chaccour1, Christo Kurisummoottil Thomas2, Walid Saad3, and Merouane Debbah4

1Ericsson, Inc., Plano, TX, USA

2Virginia Tech Research Center, Virginia Tech, Arlington, VA, USA

3Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA, USA

46G Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

1.1 From Information Streams to Streams of Understanding: The Rise of Semantic Communication Networks

Throughout history, networks have mirrored the evolution of our relationship with information. Initially, they facilitated the transmission of human voices, carrying the ephemeral nature of conversation across vast distances. Subsequently, they evolved to transport the richness of media, bringing the world into our living rooms. As technology progressed, networks connected a multitude of smaller devices, forming the nascent internet of things. However, with the dawn of 6G and beyond, and the promise of a hyper-connected future, the focus of our network infrastructure shifts from mere information transport to the interconnection of intelligence.

This fundamental shift necessitates a radical transformation in how we communicate. As we envision a world of holographic teleportation, the immersive metaverse, and the sophisticated automation of Industry 5.0, the limitations of traditional networks become increasingly apparent. These networks, acting as mere “bit pipes,” rely on brute force, pushing ever-increasing volumes of data through ever-widening bandwidths, simply cannot sustain the demands of this intelligent future. Complexity would spiral out of control, and the very infrastructure that underpins this progress would become its own Achilles’ heel.

To address this challenge effectively, we need to move away from makeshift fixes and fundamentally rethink the structure of our network. This means giving greater importance to the connections between network nodes. These connections shouldn’t just pass on data blindly; they should possess the capacity to comprehend the significance of the information they carry. This transformation, often termed as the “humanization” of the network, forms the core of semantic communication – a paradigm shift that transcends mere data transmission to facilitate the exchange of meaningful information.

It is worth emphasizing that semantic communication does not aim to replicate the full complexity of human interaction in transmitting data. Rather, its focus lies in conveying the essence of the data along with pertinent metadata, irrespective of specific data formats, and functions at the network’s foundational layers. This approach starkly contrasts with traditional wireless networks, where communication relies solely on raw, unprocessed bits. As we navigate the increasingly intricate landscape of future applications, our reliance on spectrum and coverage alone becomes unsustainable. Semantic communication represents a shift in networking philosophy toward a deeper understanding and intelligent handling of data traffic.

With the advancement of applications and the proliferation of consumer AI, the intelligence embedded within AI systems is evolving rapidly. Tasks as simple as a Google search now translate into dynamic generative pre-trained transformer (GPT) conversations, while a single recorded video can spawn numerous generated videos through AI algorithms. Moreover, the landscape is expanding with complex applications like emerging services such as next-generation extended reality (XR), full and others. In this context, it becomes imperative for networks to adapt their approach to data and traffic. Rather than adhering to the traditional “bit pipe” model, networks must mirror the intelligence embedded within applications. We envision a future where traffic continues to escalate, but instead of exponentially increasing to the point where new spectrum allocation becomes unfeasible, the concept of semantic communications intervenes to transform networks into truly AI-native entities (as shown in Figure 1.1). In this future, traffic is managed and controlled through semantic understanding, alleviating the relentless demand for spectrum that is becoming increasingly scarce.

However, by embracing semantic communication, we unlock a powerful new paradigm. We can leverage our computing resources as “in-memory” networks, reducing the strain on spectrum and enabling efficient handling of complex data. Additionally, the growing ubiquity of AI, both in consumer devices and across diverse applications, necessitates a network that can keep pace with the rising tide of data volume and intelligence. Semantic communication networks represent the crucial leap forward, enabling us to navigate this exciting future where machines not only share information but also share understanding.

Figure 1.1 Hypothesis for Taming traffic: Telecom brain and AI lock horns.

1.1.1 How Does It Work?

Semantic communication necessitates a fundamental reconsideration of the communication quandary, as delineated by Weaver’s three-tier framework [Weaver, 1953]. Following Shannon’s inception of information theory, Weaver delineated communication hurdles across three levels [Weaver, 1953]: (i) Level A, pertaining to the technical precision of communication symbols to be transmitted; (ii) Level B, addressing the semantic accuracy of transmitted symbols vis-à-vis intended meaning; and (iii) Level C, evaluating the impact of received meaning on overall system conduct. Traditional communication systems primarily tackled Level A challenges. Nonetheless, a judiciously crafted semantic communication system can leverage AI advancements and computational prowess to potentially transcend Weaver’s framework, incorporating a reasoning plane [Chaccour et al., 2024], achieving more with less. This entails a paradigm shift from conventional transmitter–receiver pairs to what we propose as teacher and apprentice nodes, endowed with the following capabilities:

• Transitioning from a bit-driven transmitter to a knowledge-driven teacher: The conventional transmitter model needs to evolve beyond merely serving as a conduit for bits of data. Instead, it should metamorphose into a teacher endowed with the ability to decipher multiple semantic content elements embedded within the source data. This entails the extraction of distinct meanings, or semantics, from the message. Subsequently, for each identified semantic content element, the teacher must generate a semantic representation possessing desirable attributes. Fundamentally, the semantic content represents the essence of the data, while the semantic representation encapsulates this essence in a minimal form – akin to how individuals meticulously select words to articulate their thoughts. Moreover, various semantic content elements may correspond to different modalities within the data. For instance, in an audio recording, the tone of voice and the spoken words constitute distinct semantic content elements. Human cognition effortlessly disentangles and comprehends these elements, a capability lacking in current communication system transmitters. Hence, there is a pressing need to reconfigure transmitters to emulate human reasoning capabilities to the best of their ability. This necessitates reasoning at the transmission end – the capacity enabling the transmitting agent to identify, differentiate, and efficiently represent each semantic content element within the data. This stands in stark contrast to the conventional approach in today’s networks, where transmitters treat input as a random, uncertain string of information transmitted through a bit-pipeline, devoid of semantic understanding. Furthermore, the shift from a data-driven to a reason-driven approach is showcased in Figure 1.2.
• Shifting from a bit-driven receiver to a knowledge-driven apprentice: Similarly, the receiver’s role should evolve from merely processing bits to embodying an apprentice endowed with reasoning abilities. This transformation enables the receiver to comprehend the minimal semantic representation utilized by the teacher, thereby mapping it back to its corresponding semantic content element. Additionally, the apprentice must harness its computational resources to accurately recreate the semantic content element derived from the transmitted semantic representation with utmost fidelity. For instance, if a holographic element is transmitted, the apprentice must possess the capability to reproduce it with identical resolution as transmitted by the teacher. Furthermore, the developed reasoning capabilities empower the apprentice to utilize both causal and associational (statistical) logic across the networking stack. These logic frameworks, derived from an evolving knowledge base, enable the apprentice to undertake diverse projections and decisions concerning the received semantic representation.

  Figure 1.2 The evolution of wireless networks from data-driven ones to reasoning-driven ones [Chaccour et al., 2024].

• Transitioning from a bit-pipeline to a semantic language: In the realm of semantic communications, the fundamental unit of meaning is encapsulated within a semantic representation. These...

Produkt-Details

• Erscheint lt. Verlag: 20.12.2024
• Sprache: englisch
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: 6G systems • 6G tech • AI-native • Artificial Intelligence (AI) • causal ai • cellular systems • generative AI • Joint-source channel coding • next-generation networks. • Semantic Communication • semantic information theory • semantic wireless • signal filtering • Signal Processing • wireless machine reasoning techniques • wireless networks • Wireless resource management
• ISBN-10: 1-394-24789-3 / 1394247893
• ISBN-13: 978-1-394-24789-9 / 9781394247899