Generative Diffusion Models for High Dimensional Channel Estimation

2024-08-20

arXiv.org

AI Summary

This work addresses the challenge of high-dimensional channel estimation in massive MIMO systems under low-bit quantized measurements. We propose an unsupervised Bayesian posterior channel estimation framework based on diffusion models—the first to employ generative diffusion models as deep priors for channel estimation. Our method integrates quantization-aware reconstruction with Stein’s unbiased risk estimator (SURE) to enable end-to-end optimization without ground-truth labels. Designed for ultra-low latency and minimal pilot overhead, it supports real-time deployment with extremely large antenna arrays. Experiments demonstrate substantial improvements: channel reconstruction fidelity is significantly enhanced, estimation latency is reduced by 10×, and pilot overhead is cut by 50%. The framework establishes a novel paradigm for efficient over-the-air learning under low-precision ADC hardware constraints.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Computer Vision: Diffusion Models for Vision

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced diffusion models (DMs), a representative class of generative AI models, to high dimensional wireless channel estimation. By capturing the structure of multiple-input multiple-output (MIMO) wireless channels via a deep generative prior encoded by DMs, we develop a novel posterior inference method for channel reconstruction. We further adapt the proposed method to recover channel information from low-resolution quantized measurements. Additionally, to enhance the over-the-air viability, we integrate the DM with the unsupervised Stein's unbiased risk estimator to enable learning from noisy observations and circumvent the requirements for ground truth channel data that is hardly available in practice. Results reveal that the proposed estimator achieves high-fidelity channel recovery while reducing estimation latency by a factor of 10 compared to state-of-the-art schemes, facilitating real-time implementation. Moreover, our method outperforms existing estimators while reducing the pilot overhead by half, showcasing its scalability to ultra-massive antenna arrays.

Problem

• Applies diffusion models to high-dimensional MIMO channel estimation.
• Recovers channel information from low-resolution quantized measurements.
• Enhances real-time viability by reducing latency and pilot overhead.

Innovation

• Uses diffusion models for high-dimensional channel estimation
• Integrates unsupervised learning for noisy data handling
• Reduces latency and pilot overhead significantly