Diffusion-based Generative Multicasting with Intent-aware Semantic Decomposition

• URL: http://arxiv.org/abs/2411.02334v1
• Date: Mon, 04 Nov 2024 17:58:54 GMT
• Title: Diffusion-based Generative Multicasting with Intent-aware Semantic Decomposition
• Authors: Xinkai Liu, Mahdi Boloursaz Mashhadi, Li Qiao, Yi Ma, Rahim Tafazolli, Mehdi Bennis,
• Abstract summary: We develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal to multiple semantic classes based on the multi-user intent. We show that the simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks.
• Score: 45.73113352988143
• License:
• Abstract: Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal to multiple semantic classes based on the multi-user intent, i.e. each user is assumed to be interested in details of only a subset of the semantic classes. The transmitter then sends to each user only its intended classes, and multicasts a highly compressed semantic map to all users over shared wireless resources that allows them to locally synthesize the other classes, i.e. non-intended classes, utilizing pre-trained diffusion models. The signal retrieved at each user is thereby partially reconstructed and partially synthesized utilizing the received semantic map. This improves utilization of the wireless resources, with better preserving privacy of the non-intended classes. We design a communication/computation-aware scheme for per-class adaptation of the communication parameters, such as the transmission power and compression rate to minimize the total latency of retrieving signals at multiple receivers, tailored to the prevailing channel conditions as well as the users reconstruction/synthesis distortion/perception requirements. The simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks while maintaining high perceptual quality of the signals retrieved at the users.

Related papers

• Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework [27.524671767937512]
  We introduce a novel Generative AI Semantic Communication (GSC) system for single-user scenarios. At the transmitter end, it employs a joint source-channel coding mechanism based on the Swin Transformer for efficient semantic feature extraction. At the receiver end, an advanced Diffusion Model (DM) reconstructs high-quality images from degraded signals, enhancing perceptual details.
  arXiv  Detail & Related papers  (2024-07-31T06:08:51Z)
• Latent Diffusion Model-Enabled Real-Time Semantic Communication Considering Semantic Ambiguities and Channel Noises [18.539501941328393]
  This paper constructs a latent diffusion model-enabled SemCom system, and proposes three improvements compared to existing works. A lightweight single-layer latent space transformation adapter completes one-shot learning at the transmitter. An end-to-end consistency distillation strategy is used to distill the diffusion models trained in latent space.
  arXiv  Detail & Related papers  (2024-06-09T23:39:31Z)
• 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)
• Neural Network Parameter Diffusion [50.85251415173792]
  Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also. generate high-performing neural network parameters.
  arXiv  Detail & Related papers  (2024-02-20T16:59:03Z)
• Reasoning with the Theory of Mind for Pragmatic Semantic Communication [62.87895431431273]
  A pragmatic semantic communication framework is proposed in this paper. It enables effective goal-oriented information sharing between two-intelligent agents. Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits.
  arXiv  Detail & Related papers  (2023-11-30T03:36:19Z)
• Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission [68.69108124451263]
  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.
  arXiv  Detail & Related papers  (2023-08-07T16:32:14Z)
• Generative Semantic Communication: Diffusion Models Beyond Bit Recovery [19.088596386865106]
  We propose a novel generative diffusion-guided framework for semantic communication. We reduce bandwidth usage by sending highly-compressed semantic information only. Our results show that objects, locations, and depths are still recognizable even in the presence of extremely noisy conditions.
  arXiv  Detail & Related papers  (2023-06-07T10:36:36Z)
• Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication [7.231199109999097]
  Non-orthogonal multiple access (NOMA)-based multi-user semantic communication system named NOMASC is proposed in this paper. System can support semantic tranmission of multiple users with diverse modalities of source information.
  arXiv  Detail & Related papers  (2023-03-12T07:32:42Z)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)

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.