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Large AI Model Empowered Multimodal Semantic Communications

Feibo Jiang, Li Dong, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Xiaohu You

arXiv:2309.01249v2cs.AIcs.CLcs.LG

TL;DR

Multimodal semantic communication must handle heterogeneous signals, semantic ambiguity, and wireless-channel distortion. The paper proposes LAM-MSC, combining multimodal alignment, personalized knowledge, and generative channel estimation; simulations report superior framework performance, while unified representation across more modalities remains challenging.

  • Problem

    Multimodal SC must address data heterogeneity, semantic ambiguity, and signal distortion while communicating text, audio, images, and video.

  • Method

    LAM-MSC combines CoDi-based MMA for multimodal transformation, GPT-4-based personalized LKB for semantic recovery, and CGE for wireless channel-gain estimation.

  • Results

    Simulations demonstrate superior performance of LAM-MSC, with personalized prompts improving semantic transmission accuracy and CGE being important to system performance.

  • Takeaways & Limitations

    LAM-MSC supports multimodal semantic communication by preserving semantic consistency, personalizing recovered meaning, and reducing fading-channel impact.

Abstract

from arXiv · show

Multimodal signals, including text, audio, image, and video, can be integrated into Semantic Communication (SC) systems to provide an immersive experience with low latency and high quality at the semantic level. However, the multimodal SC has several challenges, including data heterogeneity, semantic ambiguity, and signal distortion during transmission. Recent advancements in large AI models, particularly in the Multimodal Language Model (MLM) and Large Language Model (LLM), offer potential solutions for addressing these issues. To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, where we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency. Then, a personalized LLM-based Knowledge Base (LKB) is proposed, which allows users to perform personalized semantic extraction or recovery through the LLM. This effectively addresses the semantic ambiguity. Finally, we apply the Conditional Generative adversarial network-based channel Estimation (CGE) for estimating the wireless channel state information. This approach effectively mitigates the impact of fading channels in SC. Finally, we conduct simulations that demonstrate the superior performance of the LAM-MSC framework.

I. INTRODUCTION

Semantic Communication focuses on transmitting meaning, but multimodal applications introduce heterogeneity and semantic-alignment challenges that conventional unimodal systems handle inefficiently.

  • Semantic Communication emphasizes preserving the meaning of transmitted information rather than only reducing technical transmission errors.
  • Its typical architecture combines semantic and channel encoders, channel and semantic decoders, and a Knowledge Base for relevant semantic descriptions.
  • Multimodal applications require communication across text, voice, images, and video, whereas traditional systems generally process only one modality.
  • Using multiple unimodal systems for multimodal transmission can create significant overhead and inefficiency.
  • Multimodal SC must address heterogeneous data, complex target tasks, semantic alignment, and ambiguity caused by modality conversion and differing user knowledge.

B. Advantages of Large AI Model in Multimodal SC

Large AI models offer multimodal SC capabilities for semantic extraction, background knowledge, and interpretation. The paper applies these capabilities in a LAM-MSC framework built around multimodal alignment, personalized knowledge, and channel estimation.

  • Large AI models provide accurate semantic extraction, rich prior knowledge, and robust interpretation for multimodal SC.
  • LAM-MSC uses MMA to transform multimodal and unimodal data, LKB to personalize semantic understanding, and CGE to estimate wireless channel gains.
  • MMA employs CoDi to create a unified semantic representation through modality transformation while preserving semantic consistency.
  • LKB uses a personalized prompt base and GPT-4 to extract relevant information and reduce semantic ambiguity for individuals.
  • CGE uses a conditional generative adversarial network to estimate channel gains for multimodal semantic communication.

II. PRELIMINARIES

CoDi is a multimodal language model that generates one modality from combinations of others. Its latent diffusion process combines modality-specific conditions to guide reconstruction in a compact latent space.

  • CoDi generates text, images, video, or audio from any combination of input modalities.
  • Latent diffusion encodes data into a compact representation, reconstructs outputs in latent space, and then decodes them into data space.
  • Separate modules encapsulate modality-specific information or constraints, including textual descriptions, image features, and attributes.
  • CoDi combines these modalities or conditions to guide latent-space denoising within a single generative process.

4) Reverse Multimodal Generation:

The paper discusses GPT-4 personalization methods for adapting a global knowledge base to individual users. These methods reduce adaptation costs or guide task-specific model behavior through limited parameter or prompt changes.

  • GPT-4 is used as a large language model for semantic extraction and restoration and can support knowledge-base population from unstructured data.
  • Four personalization approaches are identified: adapter tuning, prefix tuning, prompt tuning, and LoRA.
  • Adapter tuning trains inserted adapter modules while fixing the pretrained model parameters, reducing computational costs.
  • Prefix tuning trains a small prefix that modifies pretrained-model input for task-specific behavior with fewer resources than full fine-tuning.
  • Prompt tuning guides model responses through carefully designed prompts, while LoRA adds trainable low-rank matrices and keeps original weights fixed.

C. CGAN for Channel Estimation

The framework treats channel estimation as an image-to-image translation problem and applies CGAN-based estimation to recover wireless channel gains from received signals and pilot sequences.

  • Channel estimation predicts channel characteristics such as channel gains from received data to support signal reconstruction.Accurate estimation improves communication efficiency and quality.
  • The framework reframes channel estimation as image-to-image translation because pilot sequences, received signals, and channel gains can be represented as dual-channel images.Each image represents the real and imaginary components of a complex matrix.
  • LAM-MSC integrates CGE with the multimodal semantic-communication workflow to estimate wireless channel gains.The framework includes MMA for multimodal-to-text conversion and LKB for personalized semantic processing.

1) Modal Transformation Based on MMA:

MMA converts heterogeneous multimodal inputs into text while preserving semantic alignment, allowing the sender’s intended content to be represented in a compact textual form.

  • 1) Modal Transformation Based on MMA:: MMA converts image, audio, and video inputs into text that captures the original modal content while maintaining semantic alignment.The converted text serves as a unimodal representation of the multimodal input.
  • 1) Modal Transformation Based on MMA:: An example image of Mike and Jane playing in a garden is transformed into a detailed textual description of the people, clothing, pose, and setting.The description includes attributes such as hair, clothing, and the garden background.
  • 1) Modal Transformation Based on MMA:: LKB then personalizes the transformed text using the sender’s intention, user information, and interests to retain the sender’s key semantics.The resulting description emphasizes the people and place rather than clothing details.
  • 1) Modal Transformation Based on MMA:: The semantic encoder extracts meaningful elements from the data before channel encoding and wireless transmission.This processing targets semantic information rather than transmitting every raw-data detail.

4) Semantic Recovery Based on LKB:

The receiver uses LKB to personalize decoded semantics and MMA to recover the original modality, evaluating recovery by semantic consistency rather than bit-level identity.

  • 4) Semantic Recovery Based on LKB:: LKB adapts recovered semantics to the receiver’s identity and other user information to address sender-specific semantic ambiguity.The receiver’s personalized prompt base guides this semantic transformation.
  • 4) Semantic Recovery Based on LKB:: Recovery is evaluated for semantic consistency with the original modality rather than bit-level equality.The recovered image preserves the scene of Mike and Jane playing in a garden while omitting clothing details that were not central to the sender’s intention.
  • 4) Semantic Recovery Based on LKB:: The MMA workflow uses modality-specific encoding and a condition encoder to generate target-modality data from textual semantics.The sender-side workflow similarly feeds multimodal encodings through a condition encoder before generating text.
  • 4) Semantic Recovery Based on LKB:: MMA converts personalized textual semantics back into the original multimodal form, including image, audio, or video data.The text is encoded, processed conditionally for the target modality, and then passed through the recovery process.

C. LKB

LKB combines GPT-4 with a personalized prompt base to extract and restore semantics according to user-specific information, while CGE estimates wireless channel gains through CGAN.

  • C. LKB: LKB consists of a global GPT-4 model and a personalized prompt base.These components support personalized semantic extraction and restoration.
  • C. LKB: GPT-4 provides semantic extraction and restoration through knowledge representation, attention mechanisms, and extensive pretraining.The model is described as storing rich prior and background knowledge.
  • C. LKB: Prompt tuning avoids structural modifications required by adapter tuning, prefix tuning, and LoRA, which demand specialized knowledge and substantial device resources.The paper presents these requirements as unrealistic for most ordinary users.
  • C. LKB: The personalized prompt base records user attributes such as names, ages, identities, genders, and interests in a tabular format.Users provide the prompt base together with text data to GPT-4 to generate personalized semantics.
  • C. LKB: CGE uses CGAN to estimate channel gains from received signals and pilot sequences, mitigating fading-channel effects in semantic communication.The generator learns to estimate channel information from these conditional inputs after adversarial training.

IV. SIMULATION RESULTS

The simulations evaluate multimodal semantic communication using image, audio, and video datasets, with textual semantic representations assessed by BERT-based cosine similarity. Transmission accuracy counts samples whose semantic similarity exceeds the specified threshold.

  • Model and evaluation: MMA converts image, audio, and video inputs into textual data for the textual-modal semantic communication model.The experiments use a transformer architecture because the SC model is designed for textual modal data.
  • Evaluation metric: BERT encodes textual features, while cosine similarity measures similarity between the resulting semantic vectors.Cosine similarity is used to evaluate the semantic similarity of transmitted and reference textual representations.
  • Evaluation datasets: The evaluation covers VOC2012 images, LibriSpeech speech, and UCF101 action videos.VOC2012 contains 17,125 images across 20 categories, LibriSpeech contains approximately 1,000 hours of speech, and UCF101 spans 101 action categories.
  • Evaluation metric: 0.6 is the cosine-similarity threshold for counting a transmitted sample as semantically accurate.Transmission accuracy is the ratio of semantically correct samples to all texts converted from the three modalities.

C. Evaluation Results

The evaluation shows that transmission accuracy improves with SNR, while the complete LAM-MSC benefits from personalized knowledge and channel estimation. Compared with modality-specific contenders, LAM-MSC offers stronger compression and multimodal handling, though broader universal representation remains challenging.

  • Ablation results: Transmission accuracy increases as SNR improves in the multimodal SC ablation experiments.The trend is reported for the results shown in Fig. 4.
  • Ablation results: Personalized prompts improve semantic transmission accuracy compared with LAM-MSC without LKB.The comparison attributes the observed accuracy difference to inclusion of the personalized LKB component.
  • Ablation results: LAM-MSC without CGE has the worst performance among the evaluated ablations.The authors identify this result as indicating the importance of CGE in the proposed SC system.
  • Comparison results: DeepJSCC-V and Fairseq slightly surpass LAM-MSC in transmission accuracy for their respective single modalities, while LAM-MSC has a significant compression-ratio advantage.LAM-MSC also handles multimodal information, whereas the contenders are designed for image or audio transmission.
  • Limitations: Developing a comprehensive and universal semantic representation for more modalities remains challenging.The paper identifies consistent multimodal representation as an area needing further development.
  • Limitations: Preserving semantic information during compression remains an open issue because conventional compression may lose vital context.The paper notes that extensive multimodal data requires efficient compression techniques for transmission.

2) Semantics Compression:

The paper identifies noise in multimodal data as a threat to Semantic Communication performance and highlights the need for robustness across environments. It also points to flexible, scalable processing for increasingly extensive multimodal data.

  • Noise in multimodal data may diminish the performance of Semantic Communication systems.
  • Maintaining Semantic Communication quality across varying environments remains an important research need.
  • Future Semantic Communication systems may require flexible and scalable approaches for extensive multimodal data.
  • The LAM-MSC framework addresses multimodal Semantic Communication through semantic alignment, personalized knowledge, and channel-gain estimation.
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