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Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications

Chengsi Liang, Hongyang Du, Yao Sun, Dusit Niyato, Jiawen Kang, Dezong Zhao, Muhammad Ali Imran

arXiv:2401.00124v2eess.SP

TL;DR

The paper addresses how communication networks can support demanding AIGC services despite stringent requirements and limited spectrum. It surveys GAI-driven SemCom architectures, transmission mechanisms, network management, and applications. The survey identifies potential benefits for network efficiency and user experience while outlining emerging directions for wireless communications.

  • Problem

    Future AIGC services impose stringent data-rate, throughput, and latency requirements on communication systems operating with limited spectrum resources.

  • Method

    The survey reviews GAI and SemCom fundamentals, their synergistic architecture, AIGC transmission, network management, and applications.

  • Results

    The survey presents GAI-driven SemCom as a framework spanning novel layers, knowledge management, resource allocation, and information creation and transmission for AIGC.

  • Takeaways & Limitations

    GAI-driven SemCom networks are positioned as a basis for innovative solutions that may enhance network efficiency and user experience across applications including autonomous driving, smart cities, and the Metaverse.

Abstract

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Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field demonstrating significant potential in creating diverse contents intelligently and automatically. To support such artificial intelligence-generated content (AIGC) services, future communication systems should fulfill much more stringent requirements (including data rate, throughput, latency, etc.) with limited yet precious spectrum resources. To tackle this challenge, semantic communication (SemCom), dramatically reducing resource consumption via extracting and transmitting semantics, has been deemed as a revolutionary communication scheme. The advanced GAI algorithms facilitate SemCom on sophisticated intelligence for model training, knowledge base construction and channel adaption. Furthermore, GAI algorithms also play an important role in the management of SemCom networks. In this survey, we first overview the basics of GAI and SemCom as well as the synergies of the two technologies. Especially, the GAI-driven SemCom framework is presented, where many GAI models for information creation, SemCom-enabled information transmission and information effectiveness for AIGC are discussed separately. We then delve into the GAI-driven SemCom network management involving with novel management layers, knowledge management, and resource allocation. Finally, we envision several promising use cases, i.e., autonomous driving, smart city, and the Metaverse for a more comprehensive exploration.

I. INTRODUCTION

GAI has rapidly advanced in generating diverse digital content, with broad implications for AI and communication systems.

  • GAI learns from external knowledge and patterns to produce human-like artifacts for digital content creation.
  • McKinsey estimates that GAI could add $2.6 trillion to $4.4 trillion annually to the economy.

A. Background

AIGC automatically generates digital text, images, audio, and video, increasing communication demands that conventional bit-focused systems handle inefficiently. SemCom addresses this gap by transmitting information meaning rather than reproducing source data.

  • AIGC uses machine-learning algorithms to automatically generate text, images, audio, and video.
  • GAI and AIGC services are expected to increase traffic demands and impose stricter latency and reliability requirements on wireless networks.
  • Traditional wireless systems encode all content into binary bits and neglect message meaning, resulting in low bandwidth utilization.
  • SemCom extracts hidden semantics, adapts encoding bits to channel conditions, and exchanges information meaning instead of reproducing source data.

B. Motivations

The paper motivates integrating GAI with SemCom because AIGC creates demanding multimodal workloads, while GAI can enhance semantic processing, adaptation, and network management. It identifies open challenges in multimodal processing, effectiveness measurement, and coordinated resource and knowledge management.

  • SemCom is promising for AIGC because AIGC structure and logic are tied to GAI models, while SemCom supports diverse high-volume data with sustainable resource consumption.
  • GAI strengthens SemCom encoders and decoders by generating context-sensitive, adaptable, and semantically dense content.
  • GAI can continuously refine knowledge bases and learning models so SemCom systems adapt to changing network environments.
  • Challenge 1: Multimodal SemCom must process diverse data formats while accounting for the computational time and power required for training.
  • Challenge 2: SemCom-based networks need effectiveness measures tied to achieved objectives and time because Shannon-derived indicators do not fit meaning-focused communication.
  • Challenge 3: GAI-driven SemCom requires coordinated computation, communication, and control resources plus knowledge management to avoid mismatched semantic representations.

C. Related Surveys, Contributions and Organization

The paper situates its survey alongside prior work on SemCom, 6G, and GAI for mobile telecommunications, and presents a comparative summary of related surveys.

  • Prior surveys cover SemCom trends for intelligent wireless networks, SemCom implementation in 6G, and GAI challenges for mobile telecommunications.
  • Table I summarizes related surveys in comparison with the paper’s own work.

II. GENERATIVE AI-DRIVEN SEMANTIC COMMUNICATION NETWORK

The paper frames GAI-driven SemCom networks by combining GAI’s broad knowledge capabilities with SemCom’s meaning-focused transmission. It introduces AIGC, SemCom fundamentals, and prior SemCom architectures as foundations for this integration.

  • GAI applies learned knowledge across diverse tasks and domains, while AIGC uses GAI algorithms to create and modify varied content.
  • AIGC can generate personalized content rapidly and efficiently across applications including digital art, news, education, and customer service.
  • SemCom encodes and decodes meaningful information rather than duplicating source bits, prioritizing relevant information under constrained bandwidth.
  • The paper positions its network vision as a synthesis of GAI algorithms and SemCom networks, building on prior semantic communication architectures and learning strategies.

C. GAI-driven SemCom Network Architecture

The proposed architecture combines conventional wireless infrastructure with intelligent model, knowledge, and resource-management capabilities. Its physical infrastructure spans terminal devices, access points, base stations, edge servers, and central cloud servers.

  • Physical infrastructure: GAI-driven SemCom networks retain terminals, access points, base stations, edge servers, and cloud servers while adding intelligence for AIGC services.
  • Physical infrastructure: Edge nodes pre-train and fine-tune GAI models using knowledge from themselves, connected terminals, and central cloud servers.
  • Physical infrastructure: Edge nodes offload task- and environment-specific trained models to terminals and manage knowledge sharing and updates while optimizing energy and bandwidth consumption.
  • Physical infrastructure: Central cloud servers provide the storage and computation needed to employ and pre-train large-scale GAI models for AIGC services.

2) Data Plane:

The data plane covers AIGC information creation, semantic transmission, and effectiveness evaluation. It uses GAI models to create content, SemCom encoders and decoders to transmit meaning, and metrics to assess usefulness and freshness.

  • Information creation: AIGC information is created through unimodal and multimodal GAI models on the data plane.
  • Information transmission: Semantic and channel encoders extract and compress semantic information before wireless transmission, while decoders recover distorted data using shared knowledge.
  • Information effectiveness: AIGC effectiveness is evaluated through task completion, data freshness, relevance, and causal reasoning perspectives.

3) Network Control Plane:

The network control plane adds intelligent management for architecture, knowledge, and resource allocation. It uses knowledge bases for model training and adapts allocation decisions to changing network conditions and semantic requirements.

  • Control-plane functions: The control plane includes novel semantic and generation levels alongside network architecture, knowledge management, and resource allocation.
  • Knowledge management: Knowledge management constructs, shares, and updates knowledge bases using collected data such as user history and channel status.
  • Information-creation models: Figure 2 distinguishes unimodal models for one data type from multimodal models that integrate multiple data types.
  • Resource allocation: GAI automatically adjusts resource-allocation strategies as network status changes, while allocation considers energy, bandwidth, and channel–knowledge-base matching.

III. INFORMATION CREATION VIA GENERATIVE AI

The paper organizes GAI-based information creation for AIGC into unimodal and multimodal models, spanning text, vision, audio, and video inputs and outputs. It surveys representative architectures and applications across these modalities.

  • GAI information-creation models are categorized into unimodal and multimodal approaches.The overview presents these two categories in Table II.
  • Unimodal models process a single input type, covering text, vision, and audio generation tasks.Examples include text generation, image synthesis, and audio generation.
  • Text-to-text models include Seq2Seq, VAE-based, GAN-based, and Transformer-based architectures for generation, translation, summarization, and question-answering.Transformer-based models use self-attention to handle long-range dependencies in text.
  • Vision-to-vision models generate or transform images and videos using CNN, VAE, GAN, flow, diffusion, transformer, and related deep-learning architectures.Applications include image-to-image translation, super-resolution, style transfer, video inpainting, and denoising.
  • Multimodal models convert among text, images, video, audio, and speech through text-to-X, X-to-text, and voice-bot applications.Examples include DALL-E, CogVideo, Phenaki, WaveNet, AudioLM, image/video captioning, speech recognition, and conversational assistants.

C. GAI’s Role in SemCom

GAI enhances SemCom through scalable model training, dynamic knowledge-base construction, and adaptive channel management. These capabilities support more effective semantic analysis, decision-making, and communication under changing conditions.

  • Models training and fine-tuning: GAI generates large volumes of content for scalable SemCom model training, database augmentation, and semantic analysis.Its speed also supports low-latency analysis and personalized fine-tuning.
  • KB construction: GAI automates information identification and structuring from diverse sources to build dynamic, self-updating knowledge bases.These knowledge bases improve semantic analysis and decision-making through broader and more contextually intelligent information.
  • Channel adaption: GAI adapts communication channels dynamically according to content, context, and user behavior.Real-time analysis can adjust bandwidth and modulation, predict disruptions, and support anticipatory resource allocation.
  • Information effectiveness: GAI-driven SemCom requires information-effectiveness measures beyond conventional throughput, latency, packet loss, and BER metrics.The survey considers communication goals, data freshness, and causal reasoning for AIGC regimes.

1) Task-Oriented System:

Task-oriented SemCom evaluates whether transmitted information serves recipient objectives rather than merely reproducing source data. The section surveys task, freshness, value, and causal perspectives for assessing semantic effectiveness.

  • Task-oriented SemCom: Task-oriented SemCom prioritizes the usefulness of communicated data for fulfilling a specific recipient task or objective.The approach focuses on utility rather than merely delivering raw source data.
  • Task-oriented SemCom: Existing task-oriented studies examine image retrieval, machine translation, and personalized image transmission with resource-allocation objectives.The surveyed examples cover single- and multiple-modality settings, including UAV services tailored to user interests.
  • Age and value of information: AoI measures the freshness or timeliness of information received at the destination.It is used in time-sensitive applications and considered useful for SemCom optimization.
  • Age and value of information: VoI emphasizes the importance and relevance of transmitted information rather than the quantity of data.The passage presents it as a suitable metric for SemCom and relates it to freshness through an AoI penalty function.
  • Causal reasoning: GAI and SemCom complement causal reasoning by handling complex data while supporting meaning-driven, low-latency causal analysis.The surveyed approaches include emergent SemCom and causal semantic communication frameworks using causal influence or structural causal models.

A. Novel Layers in GAI-driven SemCom Architecture

GAI-driven SemCom architectures extend conventional physical and semantic layers with generation capabilities and coordinated knowledge and resource management. The survey connects these layers to personalized content generation, protected semantic exchange, and adaptive network operation.

  • Novel layers: Prior architectures include cloud-edge-mobile designs and two-tier physical-semantic networks for multimodal provisioning and semantic-aware management.These designs support multimodal semantic content, joint-source-channel coding, and AIGC acquisition.
  • Novel layers: The proposed architecture adds a generation layer to physical and semantic layers.The physical layer transmits signals, the semantic layer handles meaning and context, and the generation layer guides GAI models using SI and algorithmic parameters.
  • Novel layers: Pre-trained foundation models can be fine-tuned for feature extraction and parameter optimization to provide personalized services.The architecture also limits exposure of sensitive information by transmitting semantic instructions and prompts.
  • Knowledge management: The architecture organizes knowledge into background knowledge and common knowledge.Background knowledge contains task-specific transmitter parameters and receiver expertise, while common knowledge is held in a shared database for both endpoints.
  • Knowledge management: Knowledge management constructs, shares, and updates knowledge across communication entities using diverse data, model parameters, sensing data, and knowledge graphs.Knowledge graphs are formed through knowledge extraction, representation learning, and completion; sharing can use federated learning, while updates can use audits and version tracking.
  • Resource allocation: Resource allocation prioritizes knowledge-related metrics alongside bandwidth, spectrum, energy, computing power, storage, and memory.GAI can predict requirements and adaptively reallocate computing resources, mitigating waste and improving network performance and user satisfaction.
  • Applications: GAI-driven SemCom extracts meaningful semantic data for Metaverse applications and removes superfluous information according to user preferences and auxiliary knowledge bases.This approach is presented as a way to address the Metaverse’s data demands and improve resource efficiency.
  • Conclusion: The survey covers GAI-SemCom fundamentals, network management, resource allocation, and applications in autonomous driving, smart cities, and the Metaverse.It frames these topics as emerging directions and practical potential in wireless communications.
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