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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2025-01-01
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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 10–15 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. |
| format | Article |
| id | doaj-art-7b3fb99210e5487d8c9d2f5ad06dcffd |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of the Communications Society |
| 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 10–15 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 |