Integrating Pre-Trained Language Model with Physical Layer Communications

• URL: http://arxiv.org/abs/2402.11656v2
• Date: Fri, 28 Jun 2024 23:00:45 GMT
• Title: Integrating Pre-Trained Language Model with Physical Layer Communications
• Authors: Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee, Jay Pujara,
• Abstract summary: We introduce a practical ondevice AI communication framework, integrated with physical layer (PHY) communication functions. Our framework incorporates end-to-end training with channel noise to enhance resilience, incorporates vector quantized variational autoencoders (VQ-VAE) for efficient and robust communication, and utilizes pre-trained encoder-decoder transformers for improved generalization capabilities.
• Score: 19.20941153929975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integrating these frameworks with existing wireless systems and effectively managing noise and bit errors pose significant challenges. In this work, we introduce a practical ondevice AI communication framework, integrated with physical layer (PHY) communication functions, demonstrated through its performance on a link-level simulator. Our framework incorporates end-to-end training with channel noise to enhance resilience, incorporates vector quantized variational autoencoders (VQ-VAE) for efficient and robust communication, and utilizes pre-trained encoder-decoder transformers for improved generalization capabilities. Simulations, across various communication scenarios, reveal that our framework achieves a 50% reduction in transmission size while demonstrating substantial generalization ability and noise robustness under standardized 3GPP channel models.

Related papers

• Large Generative Model-assisted Talking-face Semantic Communication System [55.42631520122753]
  This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) system. Generative Semantic Extractor (GSE) at the transmitter converts semantically sparse talking-face videos into texts with high information density. Private Knowledge Base (KB) based on the Large Language Model (LLM) for semantic disambiguation and correction. Generative Semantic Reconstructor (GSR) that utilizes BERT-VITS2 and SadTalker models to transform text back into a high-QoE talking-face video.
  arXiv  Detail & Related papers  (2024-11-06T12:45:46Z)
• Generative Semantic Communication for Text-to-Speech Synthesis [39.8799066368712]
  This paper develops a novel generative semantic communication framework for text-to-speech synthesis. We employ a transformer encoder and a diffusion model to achieve efficient semantic coding without introducing significant communication overhead.
  arXiv  Detail & Related papers  (2024-10-04T14:18:31Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Adaptive Semantic Token Selection for AI-native Goal-oriented Communications [11.92172357956248]
  We propose a novel design for AI-native goal-oriented communications. We exploit transformer neural networks under dynamic inference constraints on bandwidth and computation. We show that our model improves over state-of-the-art token selection mechanisms.
  arXiv  Detail & Related papers  (2024-04-25T13:49:50Z)
• Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
  We propose a novel agent-driven generative semantic communication framework based on reinforcement learning. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
  arXiv  Detail & Related papers  (2024-04-10T13:24:27Z)
• Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts [89.04751776308656]
  This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding. In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
  arXiv  Detail & Related papers  (2023-09-05T23:24:56Z)
• Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks [2.846642778157227]
  This paper emphasizes the pivotal role of achieving model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. We propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation.
  arXiv  Detail & Related papers  (2023-06-09T20:46:31Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Semantic and Effective Communication for Remote Control Tasks with Dynamic Feature Compression [23.36744348465991]
  Coordination of robotic swarms and the remote wireless control of industrial systems are among the major use cases for 5G and beyond systems. In this work, we consider a prototypal system in which an observer must communicate its sensory data to an actor controlling a task. We propose an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level.
  arXiv  Detail & Related papers  (2023-01-14T11:43:56Z)
• Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications [55.65768284748698]
  Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. This article aims to provide a holistic overview of relevant communication and ML principles, and thereby present communication-efficient and distributed learning frameworks with selected use cases.
  arXiv  Detail & Related papers  (2020-08-06T12:37:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.