Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models

Diffusion models are at the vanguard of generative AI research with renowned solutions such as ImageGen by Google Brain and DALL.E 3 by OpenAI. Nevertheless, the potential merits of diffusion models for communication engineering applications are not fully understood yet. In this paper, we aim to unleash the power of generative AI for PHY design of constellation symbols in communication systems. Although the geometry of constellations is predetermined according to networking standards, e.g., quadrature amplitude modulation (QAM), probabilistic shaping can design the probability of occurrence (generation) of constellation symbols. This can help improve the information rate and decoding performance of communication systems. We exploit the ``denoise-and-generate'' characteristics of denoising diffusion probabilistic models (DDPM) for probabilistic constellation shaping. The key idea is to learn generating constellation symbols out of noise, ``mimicking'' the way the receiver performs symbol reconstruction. This way, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible, resulting in as few mismatches as possible. Our results show that the generative AI-based scheme outperforms deep neural network (DNN)-based benchmark and uniform shaping, while providing network resilience as well as robust out-of-distribution performance under low-SNR regimes and non-Gaussian assumptions. Numerical evaluations highlight 30% improvement in terms of cosine similarity and a threefold improvement in terms of mutual information compared to DNN-based approach for 64-QAM geometry.

Denoising Diffusion Probabilistic Models for Hardware-Impaired Communication Systems: Towards Wireless Generative AI

Thanks to the outstanding achievements from state-of-the-art generative models like ChatGPT and diffusion models, generative AI has gained substantial attention across various industrial and academic domains. In this paper, denoising diffusion probabilistic models (DDPMs) are proposed for a practical finite-precision wireless communication system with hardware-impaired transceivers. The intuition behind DDPM is to decompose the data generation process over the so-called "denoising" steps. Inspired by this, a DDPM-based receiver is proposed for a practical wireless communication scheme that faces realistic non-idealities, including hardware impairments (HWI), channel distortions, and quantization errors. It is shown that our approach provides network resilience under low-SNR regimes, near-invariant reconstruction performance with respect to different HWI levels and quantization errors, and robust out-of-distribution performance against non-Gaussian noise. Moreover, the reconstruction performance of our scheme is evaluated in terms of cosine similarity and mean-squared error (MSE), highlighting more than 25 dB improvement compared to the conventional deep neural network (DNN)-based receivers.

WiGenAI: The Symphony of Wireless and Generative AI via Diffusion Models

Innovative foundation models, such as GPT-3 and stable diffusion models, have made a paradigm shift in the realm of artificial intelligence (AI) towards generative AI-based systems. In unison, from data communication and networking perspective, AI and machine learning (AI/ML) algorithms are envisioned to be pervasively incorporated into the future generations of wireless communications systems, highlighting the need for novel AI-native solutions for the emergent communication scenarios. In this article, we outline the applications of generative AI in wireless communication systems to lay the foundations for research in this field. Diffusion-based generative models, as the new state-of-the-art paradigm of generative models, are introduced, and their applications in wireless communication systems are discussed. Two case studies are also presented to showcase how diffusion models can be exploited 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. As the second application, DDPMs are employed at the transmitter to shape the constellation symbols, highlighting a robust out-of-distribution performance. Finally, future directions and open issues for the development of generative AI-based wireless systems are discussed to promote future research endeavors towards wireless generative AI (WiGenAI).

Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications

Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models such as generative pre-trained transformers (GPT) and diffusion models. In this paper, we explore the applications of denoising diffusion probabilistic models (DDPMs) in wireless communication systems under practical assumptions such as hardware impairments (HWI), low-SNR regime, and quantization error. Diffusion models are a new class of state-of-the-art generative models that have already showcased notable success with some of the popular examples by OpenAI and Google Brain. The intuition behind DDPM is to decompose the data generation process over small "denoising" steps. Inspired by this, we propose using denoising diffusion model-based receiver for a practical wireless communication scheme, while providing network resilience in low-SNR regimes, non-Gaussian noise, different HWI levels, and quantization error. We evaluate the reconstruction performance of our scheme in terms of bit error rate (BER) and mean-squared error (MSE). Our results show that 30% and 20% improvement in BER could be achieved compared to deep neural network (DNN)-based receivers in AWGN and non-Gaussian scenarios, respectively.

Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach

With the incredible results achieved from generative pre-trained transformers (GPT) and diffusion models, generative AI (GenAI) is envisioned to yield remarkable breakthroughs in various industrial and academic domains. In this paper, we utilize denoising diffusion probabilistic models (DDPM), as one of the state-of-the-art generative models, for probabilistic constellation shaping in wireless communications. While the geometry of constellations is predetermined by the networking standards, probabilistic constellation shaping can help enhance the information rate and communication performance by designing the probability of occurrence (generation) of constellation symbols. Unlike conventional methods that deal with an optimization problem over the discrete distribution of constellations, we take a radically different approach. Exploiting the ``denoise-and-generate'' characteristic of DDPMs, the key idea is to learn how to generate constellation symbols out of noise, ``mimicking'' the way the receiver performs symbol reconstruction. By doing so, we make the constellation symbols sent by the transmitter, and what is inferred (reconstructed) at the receiver become as similar as possible. Our simulations show that the proposed scheme outperforms deep neural network (DNN)-based benchmark and uniform shaping, while providing network resilience as well as robust out-of-distribution performance under low-SNR regimes and non-Gaussian noise. Notably, a threefold improvement in terms of mutual information is achieved compared to DNN-based approach for 64-QAM geometry.

Secure Deep-JSCC Against Multiple Eavesdroppers

In this paper, a generalization of deep learning-aided joint source channel coding (Deep-JSCC) approach to secure communications is studied. We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers over complex-valued fading channels. Both scenarios of colluding and non-colluding eavesdroppers are studied. For the colluding strategy, eavesdroppers share their logits to collaboratively infer private attributes based on ensemble learning method, while for the non-colluding setup they act alone. The goal is to prevent eavesdroppers from inferring private (sensitive) information about the transmitted images, while delivering the images to a legitimate receiver with minimum distortion. By generalizing the ideas of privacy funnel and wiretap channel coding, the trade-off between the image recovery at the legitimate node and the information leakage to the eavesdroppers is characterized. To solve this secrecy funnel framework, we implement deep neural networks (DNNs) to realize a data-driven secure communication scheme, without relying on a specific data distribution. Simulations over CIFAR-10 dataset verifies the secrecy-utility trade-off. Adversarial accuracy of eavesdroppers are also studied over Rayleigh fading, Nakagami-m, and AWGN channels to verify the generalization of the proposed scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.