Modulated-Signal-Based EM/RF/DSP Co-Simulation Framework for Predictive Analysis of Fully Digital MIMO Transmitters
Bibliographic record
Abstract
This paper proposes a comprehensive electromagnetic/radio-frequency/digital signal processing (EM/RF/DSP) co-simulation framework that enables predictive analysis of fully digital multiple-input multiple-output (MIMO) transmitters using modulated signals. It seamlessly integrates complementary EM, RF and DSP simulators to analyze the effects of design choices at the level of RF circuits and DSP algorithms, and their interactions. The usefuleness of the proposed framework is experimentally validated by evaluating its ability to predict both DC and RF performance of a custom-built 3.4 GHz 2 × 2 Fully Digital MIMO transmitter (TX) driven with 100 MHz modulated signals. Experiments conducted with and without the application of single-input single-output (SISO) and dual-input single-output (DISO) digital predistortion (DPD) schemes revealed an excellent agreement between the simulated and measured adjacent channel power ratio (ACPR) and root normalized mean-square error (RNMSE). Building on this promising outcome, studies of antenna cross-coupling and antenna impedance modulation of the fully digital MIMO TX are conducted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".