DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation

Real-time multiple-input multiple-output (MIMO) channel estimation poses a major challenge due to stringent low-latency constraints. We propose a multistep fast ordinary differential equation (ODE)-based diffusion probabilistic model (DPM), DPM-Solver-2M, for MIMO channel estimation, significantly r...

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Main Authors: Ravi Kumar, Manivasakan Rathinam
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of the Communications Society
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Online Access:https://ieeexplore.ieee.org/document/11016071/
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author Ravi Kumar
Manivasakan Rathinam
author_facet Ravi Kumar
Manivasakan Rathinam
author_sort Ravi Kumar
collection DOAJ
description Real-time multiple-input multiple-output (MIMO) channel estimation poses a major challenge due to stringent low-latency constraints. We propose a multistep fast ordinary differential equation (ODE)-based diffusion probabilistic model (DPM), DPM-Solver-2M, for MIMO channel estimation, significantly reducing the number of inference steps while maintaining high accuracy. Unlike conventional discrete-time DPMs, our approach reformulates a lightweight discrete noise prediction model into a continuous-time framework, enabling ODE-based fast multistep solvers with efficient numerical methods. This scheme retains the advantages of discrete models, such as low complexity, while achieving a <inline-formula> <tex-math notation="LaTeX">$\sim 4\times $ </tex-math></inline-formula> speedup in terms of inference steps or number of function evaluations (NFE) during the reverse process over Markovian or ancestral sampling-based DPM estimators, with only a marginal performance trade-off. Theoretical analysis of solver convergence corroborates our simulation results, demonstrating rapid convergence in just 10&#x2013;15 solver steps, making our approach highly suitable for real-time wireless systems. In addition to achieving strong performance under ideal conditions, our simulation results reveal that the proposed model is robust to changes in the signal-to-noise ratio (SNR) of the observed (received) signal and generalizes well across different wireless channel models for the small number of steps.
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spelling doaj-art-7b3fb99210e5487d8c9d2f5ad06dcffd2025-08-20T02:32:41ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164742475510.1109/OJCOMS.2025.357408711016071DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel EstimationRavi Kumar0https://orcid.org/0009-0005-4825-7045Manivasakan Rathinam1https://orcid.org/0000-0001-8001-9366Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, IndiaDepartment of Electrical Engineering, Indian Institute of Technology Madras, Chennai, IndiaReal-time multiple-input multiple-output (MIMO) channel estimation poses a major challenge due to stringent low-latency constraints. We propose a multistep fast ordinary differential equation (ODE)-based diffusion probabilistic model (DPM), DPM-Solver-2M, for MIMO channel estimation, significantly reducing the number of inference steps while maintaining high accuracy. Unlike conventional discrete-time DPMs, our approach reformulates a lightweight discrete noise prediction model into a continuous-time framework, enabling ODE-based fast multistep solvers with efficient numerical methods. This scheme retains the advantages of discrete models, such as low complexity, while achieving a <inline-formula> <tex-math notation="LaTeX">$\sim 4\times $ </tex-math></inline-formula> speedup in terms of inference steps or number of function evaluations (NFE) during the reverse process over Markovian or ancestral sampling-based DPM estimators, with only a marginal performance trade-off. Theoretical analysis of solver convergence corroborates our simulation results, demonstrating rapid convergence in just 10&#x2013;15 solver steps, making our approach highly suitable for real-time wireless systems. In addition to achieving strong performance under ideal conditions, our simulation results reveal that the proposed model is robust to changes in the signal-to-noise ratio (SNR) of the observed (received) signal and generalizes well across different wireless channel models for the small number of steps.https://ieeexplore.ieee.org/document/11016071/ODE solverDPMinference stepslow-latency channel estimationMIMONFE
spellingShingle Ravi Kumar
Manivasakan Rathinam
DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation
IEEE Open Journal of the Communications Society
ODE solver
DPM
inference steps
low-latency channel estimation
MIMO
NFE
title DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation
title_full DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation
title_fullStr DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation
title_full_unstemmed DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation
title_short DPM-Solver-2M: A Fast Multistep DPM-Solver-Based Scheme for Real-Time MIMO Channel Estimation
title_sort dpm solver 2m a fast multistep dpm solver based scheme for real time mimo channel estimation
topic ODE solver
DPM
inference steps
low-latency channel estimation
MIMO
NFE
url https://ieeexplore.ieee.org/document/11016071/
work_keys_str_mv AT ravikumar dpmsolver2mafastmultistepdpmsolverbasedschemeforrealtimemimochannelestimation
AT manivasakanrathinam dpmsolver2mafastmultistepdpmsolverbasedschemeforrealtimemimochannelestimation