Antenna De-Embedding Using Deconvolution With Tikhonov Regularization for mmWave Channel Measurement
Bibliographic record
Abstract
Antenna-free channel models can reflect real multipath propagation and can be applied widely for performance analysis, simulation, and physical emulation, combined with specific antennas used in the communication systems. However, the millimeter-wave (mmWave) channel measurement is usually performed by steering horn antennas, and the measured channel responses are actually spatial convolution of the channel propagation models and antenna pattern, which is commonly referred to as the antenna embedding effect. In this work, we propose a novel antenna de-embedding algorithm based on the deconvolution with Tikhonov regularization. By suppressing parts of the observed responses which are disguised by noise, the Tikhonov regularization facilitates the deconvolution of antenna pattern and enables the extraction of propagation models. In particular, in order to minimize the impact of deconvolved noise, we design an optimization algorithm to obtain the appropriate regularization factor with low computational complexity. To validate the proposed approach, we have performed an indoor mmWave channel measurement campaign using two different steering horn antennas. The principal peaks in the synthesized channel responses are accurately reconstructed, and the signal-to-noise ratio (SNR) is improved. The experiments verify that the proposed scheme de-embeds effectively the antenna effect and leads to the antenna-free channel models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".