Try OpenDPD Studio in your browser →
No installation. Explore example data or upload your own CSV, train PA/DPD models on shared CUDA compute, and download your checkpoints. During the trial, 2 hours without user activity clears that IP’s temporary workspaces; all data is deleted within 12 hours.
OpenDPD¶
Model a power amplifier. Train a digital predistorter. Understand the result.
OpenDPD is a PyTorch framework for power amplifier (PA) modeling and digital predistortion (DPD), developed by the Efficient Machine Intelligence Lab at TU Delft. Use OpenDPD Studio in the browser or locally for a guided workflow, or automate experiments with the CLI and Python API. All three use the original OpenDPD training core.
Try Studio now Install Studio Run from a terminal
What's new¶
OpenDPD 2.4.2 reduces hosted VM memory residency while retaining the same 8 GiB guest capacity, compute limits and numerical behavior. See the 2.4.2 release notes.
OpenDPD 2.4.1 adds the NR FR1 standard test models of 3GPP TS 38.141-1 to the Signal Generator: NR-FR1-TM1.1, 1.2, 2, 2a, 2b, 3.1, 3.1a, 3.1b, 3.2, 3.3 and NR-N-TM, as FDD or TDD baseband signals of 15/30/60 kHz and 3–100 MHz with one to sixteen carriers. The 5G NR tab opens on the model picker and lists what the standard fixes; the generic preset matrix is behind a button. With the data convention set to MATLAB's, the resource grids agree with MATLAB R2026a's 5G Toolbox in all 438 compared configurations (414 bit-identical, the rest within 3.2e-16); the default convention follows the standard's text and differs from MATLAB's random bits for TDD, TM2/2a/2b and captures longer than one frame. The signals are uncoded baseband stimuli (PDCCH, PDSCH and DM-RS); at default settings 35 of the 80 combinations of duplex mode, subcarrier spacing and bandwidth (10 of 40 FDD, 25 of 40 TDD) of a single carrier are shorter than the standard's 10 or 20 ms, because a capture holds at most 1,000,000 samples; the EVM shown is a known-grid diagnostic, not a conformance claim.
OpenDPD 2.4.0 added the MATLAB toolbox, MATLINK and a Python SDK: train PA/DPD models from MATLAB, export portable model packages, and verify them in plain MATLAB. Two versioned LTE EVM/ACLR profiles pass all 115 registered digital-signal comparisons with MATLAB; the original v1 failure record remains available. DPD Arena, introduced in 2.3.0, compares 23 DPD model entries on APA_200MHz_b across parameter budgets using test EVM, output ACLR, arithmetic cost and Pareto fronts. Results are predictions through one frozen PA model, not new hardware measurements.
Signal Generator → PA Library → PA training → DPD training/testing. Generate a PA input, simulate its output with one of nine Virtual PAs, or use existing input/output data. Matrix presets are uncoded engineering stimuli and the NR test models are uncoded baseband implementations of the standard's definitions; each capture keeps its own sample rate and length.
2.4.1 release notes · NR standard test models · MATLAB toolbox · Signal Generator · Signal Analyzer. During the hosted trial, 2 hours of inactivity clears that IP’s temporary workspaces; the top bar shows the expiry time.
Find your path¶
-
Your first experiment
Generate a waveform, choose a Virtual PA and make paired data, or start with an existing dataset. Follow the PA → DPD workflow.
-
Automate your work
Run recipes from a terminal, use the Python API, or explore a notebook.
CLI · Examples & Colab · API
-
Understand the results
Learn how data, PA modeling and DPD training connect, and inspect the plots.
-
Go further
Compare models, reproduce a paper, or evaluate measured DPD and deployment.
Project¶
What's new · FAQ · Team & research · Citation · Contributing