# DiffPace: Diffusion-based Plug-and-play Augmented Channel Estimation in mmWave and Terahertz Ultra-Massive MIMO Systems

arXiv: https://arxiv.org/abs/2511.01867 · HTML: https://mlsanity.com/paper/2511.01867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 21 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [High Dimensional Channel Estimation Using Deep Generative Networks](https://mlsanity.com/paper/2006.13494/) (2020)
- [Large AI Model-Based Semantic Communications](https://mlsanity.com/paper/2307.03492/) (2023)
- [Evaluating the design space of diffusion-based generative models](https://mlsanity.com/paper/2406.12839/) (2024)
- [Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation](https://mlsanity.com/paper/2403.03545/) (2024)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://mlsanity.com/paper/2106.05370/) (2021)
- [AI-RAN: Transforming RAN with AI-driven Computing Infrastructure](https://mlsanity.com/paper/2501.09007/) (2025)
- [An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation](https://mlsanity.com/paper/2211.15939/) (2022)
- [Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems](https://mlsanity.com/paper/2101.08469/) (2021)
- [Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems](https://mlsanity.com/paper/2106.14792/) (2021)
- [Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?](https://mlsanity.com/paper/2109.07883/) (2021)
- [Combating the Distance Problem in the Millimeter Wave and Terahertz Frequency Bands](https://mlsanity.com/paper/1902.04682/) (2019)
- [Terahertz Wireless Channels: A Holistic Survey on Measurement, Modeling, and Analysis](https://mlsanity.com/paper/2111.04522/) (2021)
- [Near-Field Channel Estimation in Mixed LoS/NLoS Environments for Extremely Large-Scale MIMO Systems](https://mlsanity.com/paper/2205.03615/) (2022)
- [Hybrid Spherical- and Planar-Wave Channel Modeling and Estimation for Terahertz Integrated UM-MIMO and IRS Systems](https://mlsanity.com/paper/2205.13113/) (2022)
- [Full-Duplex Millimeter Wave MIMO Channel Estimation: A Neural Network Approach](https://mlsanity.com/paper/2402.03886/) (2024)

## Abstract (arXiv)

Millimeter-wave (mmWave) and Terahertz (THz)-band communications hold great promise in meeting the growing data-rate demands of next-generation wireless networks, offering abundant bandwidth. To mitigate the severe path loss inherent to these high frequencies and reduce hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) systems with hybrid beamforming architectures can deliver substantial beamforming gains and enhanced spectral efficiency. However, accurate channel estimation (CE) in mmWave and THz UM-MIMO systems is challenging due to high channel dimensionality and compressed observations from a limited number of RF chains, while the hybrid near- and far-field radiation patterns, arising from large array apertures and high carrier frequencies, further complicate CE. Conventional compressive sensing based frameworks rely on predefined sparsifying matrices, which cannot faithfully capture the hybrid near-field and far-field channel structures, leading to degraded estimation performance. This paper introduces DiffPace, a diffusion-based plug-and-play method for channel estimation. DiffPace uses a diffusion model (DM) to capture the channel distribution based on the hybrid spherical and planar-wave (HPSM) model. By applying the plug-and-play approach, it leverages the DM as prior knowledge, improving CE accuracy. Moreover, DM performs inference by solving an ordinary differential equation, minimizing the number of required inference steps compared with stochastic sampling method. Experimental results show that DiffPace achieves competitive CE performance, attaining -15 dB normalized mean square error (NMSE) at a signal-to-noise ratio (SNR) of 10 dB, with 90% fewer inference steps compared to state-of-the-art schemes, simultaneously providing high estimation precision and enhanced computational efficiency.