Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks
- URL: http://arxiv.org/abs/2407.20970v1
- Date: Tue, 30 Jul 2024 16:57:41 GMT
- Title: Large Language Models (LLMs) for Semantic Communication in Edge-based IoT Networks
- Authors: Alakesh Kalita,
- Abstract summary: Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters.
LLMs can be used under the umbrella of semantic communication at the network edge for efficient communication in IoT networks.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the advent of Fifth Generation (5G) and Sixth Generation (6G) communication technologies, as well as the Internet of Things (IoT), semantic communication is gaining attention among researchers as current communication technologies are approaching Shannon's limit. On the other hand, Large Language Models (LLMs) can understand and generate human-like text, based on extensive training on diverse datasets with billions of parameters. Considering the recent near-source computational technologies like Edge, in this article, we give an overview of a framework along with its modules, where LLMs can be used under the umbrella of semantic communication at the network edge for efficient communication in IoT networks. Finally, we discuss a few applications and analyze the challenges and opportunities to develop such systems.
Related papers
- Token Communications: A Unified Framework for Cross-modal Context-aware Semantic Communications [78.80966346820553]
We introduce token communications (TokCom), a unified framework to leverage cross-modal context information in generative semantic communications (GenSC)
TokCom is motivated by the recent success of generative foundation models and multimodal large language models (GFM/MLLMs)
We demonstrate the corresponding TokCom benefits in a GenSC setup for image, leveraging cross-modal context information, which increases the bandwidth efficiency by 70.8% with negligible loss of semantic/perceptual quality.
arXiv Detail & Related papers (2025-02-17T18:14:18Z) - A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions [37.427638898804055]
Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks.
This survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks.
arXiv Detail & Related papers (2024-12-16T20:01:36Z) - NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Models [11.015852090523229]
Large language models (LLMs) have revolutionized various domains by leveraging their sophisticated natural language understanding capabilities.
This paper presents NETORCHLLM, a wireless NETwork ORCHestrator LLM framework that seamlessly orchestrates diverse wireless-specific models.
A comprehensive framework is introduced, demonstrating the practical viability of our approach.
arXiv Detail & Related papers (2024-12-13T12:48:15Z) - Large Language Model Enhanced Multi-Agent Systems for 6G Communications [94.45712802626794]
We propose a multi-agent system with customized communication knowledge and tools for solving communication related tasks using natural language.
We validate the effectiveness of the proposed multi-agent system by designing a semantic communication system.
arXiv Detail & Related papers (2023-12-13T02:35:57Z) - Accordion: A Communication-Aware Machine Learning Framework for Next
Generation Networks [8.296411540693706]
We advocate for the design of ad hoc artificial intelligence (AI)/machine learning (ML) models to facilitate their usage in future smart infrastructures based on communication networks.
We present a novel communication-aware ML framework, which enables an efficient AI/ML model transfer thanks to an overhauled model training and communication protocol.
arXiv Detail & Related papers (2023-01-12T10:30:43Z) - Less Data, More Knowledge: Building Next Generation Semantic
Communication Networks [180.82142885410238]
We present the first rigorous vision of a scalable end-to-end semantic communication network.
We first discuss how the design of semantic communication networks requires a move from data-driven networks towards knowledge-driven ones.
By using semantic representation and languages, we show that the traditional transmitter and receiver now become a teacher and apprentice.
arXiv Detail & Related papers (2022-11-25T19:03:25Z) - Distributed Learning in Wireless Networks: Recent Progress and Future
Challenges [170.35951727508225]
Next-generation wireless networks will enable many machine learning (ML) tools and applications to analyze various types of data collected by edge devices.
Distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges.
This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
arXiv Detail & Related papers (2021-04-05T20:57:56Z) - A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning [115.75967665222635]
Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks.
This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
arXiv Detail & Related papers (2020-09-13T14:53:01Z) - 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.