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

• 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)
• Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels [11.108614988357008]
  We propose an interference-robust semantic communication (IRSC) scheme for general multiple-input multiple-output (MIMO) interference channels. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches.
  arXiv  Detail & Related papers  (2024-04-10T11:40:22Z)
• Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models [43.27015039765803]
  We develop a latency-aware semantic communications framework with pre-trained generative models. We demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications.
  arXiv  Detail & Related papers  (2024-03-25T23:04:09Z)
• Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning [49.83882366499547]
  This paper studies task-oriented, otherwise known as goal-oriented, communications in a setting where a transmitter communicates with multiple receivers. A multi-task deep learning approach is presented for joint optimization of completing multiple tasks and communicating with multiple receivers.
  arXiv  Detail & Related papers  (2023-08-14T01:34:34Z)
• Enabling the Wireless Metaverse via Semantic Multiverse Communication [82.47169682083806]
  Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems. We propose a novel semantic communication framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs) An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI)
  arXiv  Detail & Related papers  (2022-12-13T21:21:07Z)
• SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks [56.68149211499535]
  Federated learning (FL) is a key enabler for efficient communication and computing leveraging devices' distributed computing capabilities. This paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNNs) We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2022-03-26T15:06:13Z)
• 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.