Diffusion Models for Wireless Communications
- URL: http://arxiv.org/abs/2310.07312v3
- Date: Fri, 1 Dec 2023 11:38:20 GMT
- Title: Diffusion Models for Wireless Communications
- Authors: Mehdi Letafati, Samad Ali, and Matti Latva-aho
- Abstract summary: We outline the applications of diffusion models in wireless communication systems.
The key idea is to decompose data generation process over "denoising" steps, gradually generating samples out of noise.
We show how diffusion models can be employed for the development of resilient AI-native communication systems.
- Score: 12.218161437914118
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Innovative foundation models, such as GPT-4 and stable diffusion models, have
made a paradigm shift in the realm of artificial intelligence (AI) towards
generative AI-based systems. AI and machine learning (AI/ML) algorithms are
envisioned to be pervasively incorporated into the future wireless
communications systems. In this article, we outline the applications of
diffusion models in wireless communication systems, which are a new family of
probabilistic generative models that have showcased state-of-the-art
performance. The key idea is to decompose data generation process over
"denoising" steps, gradually generating samples out of noise. Based on two case
studies presented, we show how diffusion models can be employed for the
development of resilient AI-native communication systems. Specifically, we
propose denoising diffusion probabilistic models (DDPM) for a wireless
communication scheme with non-ideal transceivers, where 30% improvement is
achieved in terms of bit error rate. In the other example, DDPM is employed at
the transmitter to shape the constellation symbols, highlighting a robust
out-of-distribution performance.
Related papers
- Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling [2.91204440475204]
Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models.
They rely on sequential denoising steps during sample generation.
We propose a novel method that integrates denoising phases directly into the model's architecture.
arXiv Detail & Related papers (2024-05-31T08:19:44Z) - Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI)
In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion)
Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
arXiv Detail & Related papers (2024-02-15T18:59:18Z) - Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High
Spectrum Efficiency [46.92279990929111]
This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models.
We propose a new low-dimensional noise space for the training of diffusion models, which significantly reduces the communication overhead.
Our experimental results demonstrate that the proposed noise space and the diffusion-based generative model achieve ultra-high spectrum efficiency and accurate recovery of transmitted image signals.
arXiv Detail & Related papers (2023-11-13T17:52:44Z) - Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
We propose score entropy as a novel loss that naturally extends score matching to discrete spaces.
We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
arXiv Detail & Related papers (2023-10-25T17:59:12Z) - Over-the-Air Federated Learning and Optimization [52.5188988624998]
We focus on Federated learning (FL) via edge-the-air computation (AirComp)
We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings.
For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error.
In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
arXiv Detail & Related papers (2023-10-16T05:49:28Z) - Adversarial Training of Denoising Diffusion Model Using Dual
Discriminators for High-Fidelity Multi-Speaker TTS [0.0]
The diffusion model is capable of generating high-quality data through a probabilistic approach.
It suffers from the drawback of slow generation speed due to the requirement of a large number of time steps.
We propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data.
arXiv Detail & Related papers (2023-08-03T07:22:04Z) - Phoenix: A Federated Generative Diffusion Model [6.09170287691728]
Training generative models on large centralized datasets can pose challenges in terms of data privacy, security, and accessibility.
This paper proposes a novel method for training a Denoising Diffusion Probabilistic Model (DDPM) across multiple data sources using Federated Learning (FL) techniques.
arXiv Detail & Related papers (2023-06-07T01:43:09Z) - A Cheaper and Better Diffusion Language Model with Soft-Masked Noise [62.719656543880596]
Masked-Diffuse LM is a novel diffusion model for language modeling, inspired by linguistic features in languages.
Specifically, we design a linguistic-informed forward process which adds corruptions to the text through strategically soft-masking to better noise the textual data.
We demonstrate that our Masked-Diffuse LM can achieve better generation quality than the state-of-the-art diffusion models with better efficiency.
arXiv Detail & Related papers (2023-04-10T17:58:42Z) - Diffusion Models in Vision: A Survey [80.82832715884597]
A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage.
Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens.
arXiv Detail & Related papers (2022-09-10T22:00:30Z) - A Survey on Generative Diffusion Model [75.93774014861978]
Diffusion models are an emerging class of deep generative models.
They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space.
This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
arXiv Detail & Related papers (2022-09-06T16:56:21Z) - Multi-task Learning Approach for Modulation and Wireless Signal
Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices.
We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks.
We provide a comprehensive heterogeneous wireless signals dataset for public use.
arXiv Detail & Related papers (2022-02-26T14:51:02Z)
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.