Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models

• URL: http://arxiv.org/abs/2311.09349v1
• Date: Wed, 15 Nov 2023 20:14:21 GMT
• Title: Generative AI-Based Probabilistic Constellation Shaping With Diffusion Models
• Authors: Mehdi Letafati, Samad Ali, and Matti Latva-aho
• Abstract summary: We aim to unleash the power of generative AI for PHY design of constellation symbols in communication systems. We exploit the denoise-and-generate'' characteristics of diffusion probabilistic models (DDPM) for probabilistic constellation shaping. Our results show that the generative AI-based scheme outperforms deep neural network (DNN)-based benchmark and uniform shaping.
• Score: 12.218161437914118
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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