Active Calibration Approach Addressing Antenna Mutual Coupling and Power Amplifier Output Mismatch in Fully Digital MIMO Transmitters
Bibliographic record
Abstract
This article introduces an active calibration scheme tailored for fully digital multiple-input multiple-output (MIMO) transmitters, a key step toward ensuring channel reciprocity. The theoretical analysis starts by examining the influence of antenna mutual coupling and power amplifier (PA) output impedances on the MIMO transmitter and channel reciprocity. This analysis highlights the significant impact of the inherently poor output matching, exhibited in high-efficiency PAs, exacerbating the effects of antenna mutual coupling on channel reciprocity. Consequently, an active calibration scheme is formulated to concurrently characterize and compensate for the nonflat responses of all radio frequency (RF) chains in the fully digital MIMO transmitter. To validate the proposed scheme, a proof-of-concept experiment is conducted using a custom-built 16-chain fully digital MIMO system, driven with 200-MHz orthogonal frequency-division multiplexing (OFDM) signals at 3.5-GHz center frequency. Measurement results demonstrate the efficacy of the calibration scheme in mitigating the impact of antenna coupling and PA output mismatch on channel reciprocity. The root normalized mean square error (RNMSE) after calibration is reduced from${22\%}$to${1\%}$.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".