Communication-Efficient Framework for Distributed Image Semantic
Wireless Transmission
- URL: http://arxiv.org/abs/2308.03713v2
- Date: Tue, 8 Aug 2023 02:53:52 GMT
- Title: Communication-Efficient Framework for Distributed Image Semantic
Wireless Transmission
- Authors: Bingyan Xie, Yongpeng Wu, Yuxuan Shi, Derrick Wing Kwan Ng, Wenjun
Zhang
- Abstract summary: Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices.
Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator.
Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
- Score: 68.69108124451263
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-node communication, which refers to the interaction among multiple
devices, has attracted lots of attention in many Internet-of-Things (IoT)
scenarios. However, its huge amounts of data flows and inflexibility for task
extension have triggered the urgent requirement of communication-efficient
distributed data transmission frameworks. In this paper, inspired by the great
superiorities on bandwidth reduction and task adaptation of semantic
communications, we propose a federated learning-based semantic communication
(FLSC) framework for multi-task distributed image transmission with IoT
devices. Federated learning enables the design of independent semantic
communication link of each user while further improves the semantic extraction
and task performance through global aggregation. Each link in FLSC is composed
of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive
translator for coarse-to-fine semantic extraction and meaning translation
according to specific tasks. In order to extend the FLSC into more realistic
conditions, we design a channel state information-based multiple-input
multiple-output transmission module to combat channel fading and noise.
Simulation results show that the coarse semantic information can deal with a
range of image-level tasks. Moreover, especially in low signal-to-noise ratio
and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional
scheme, e.g. about 10 peak signal-to-noise ratio gain in the 3 dB channel
condition.
Related papers
- Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation [51.53221300103261]
This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture.
A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions.
Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection.
arXiv Detail & Related papers (2025-02-12T09:01:25Z) - Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction [69.24689059980035]
This paper presents a new graph attention inter-block (GAI) module to the encoder/transmitter of a multi-task semantic communication system.
We interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features.
Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset.
arXiv Detail & Related papers (2025-01-02T04:38:01Z) - Pilot-guided Multimodal Semantic Communication for Audio-Visual Event Localization [4.680740822211451]
Multimodal semantic communication significantly enhances communication efficiency and reliability.
It has broad application prospects in fields such as artificial intelligence, autonomous driving, and smart homes.
This paper proposes a pilot-guided framework for multimodal semantic communication specifically tailored for audio-visual event localization tasks.
arXiv Detail & Related papers (2024-12-09T04:58:49Z) - AI Flow at the Network Edge [58.31090055138711]
AI Flow is a framework that streamlines the inference process by jointly leveraging the heterogeneous resources available across devices, edge nodes, and cloud servers.
This article serves as a position paper for identifying the motivation, challenges, and principles of AI Flow.
arXiv Detail & Related papers (2024-11-19T12:51:17Z) - Synchronous Multi-modal Semantic Communication System with Packet-level Coding [20.397350999784276]
We propose a Synchronous Multimodal Semantic Communication System (SyncSC) with Packet-Level Coding.
To achieve semantic and time synchronization, 3D Morphable Mode (3DMM) coefficients and text are transmitted as semantics.
To protect semantic packets under the erasure channel, we propose a packet-Level Forward Error Correction (FEC) method, called PacSC, that maintains a certain visual quality performance even at high packet loss rates.
arXiv Detail & Related papers (2024-08-08T15:42:00Z) - Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency [59.15544887307901]
Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission.
Existing ISC systems based on joint source-channel coding face challenges in interpretability, operability, and compatibility.
We propose a novel trustworthy ISC framework that employs Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks.
arXiv Detail & Related papers (2024-08-07T14:32:36Z) - Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network [70.63240823677182]
Near-space airship-borne communication network urgently needs reliable and efficient Airship-to-X link.
This paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology.
arXiv Detail & Related papers (2024-05-30T09:46:59Z) - Learning Task-Oriented Communication for Edge Inference: An Information
Bottleneck Approach [3.983055670167878]
A low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing.
It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth.
We propose a learning-based communication scheme that jointly optimize feature extraction, source coding, and channel coding.
arXiv Detail & Related papers (2021-02-08T12:53:32Z)
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.