Delta-sigma noise shaping in 2D spacetime for uniform linear aperture array receivers
Bibliographic record
Abstract
A multi-dimensional noise-shaping method based on delta-sigma modulation has been proposed. This method extends delta-sigma modulation into the two-dimensional (2-D) case (space, time). The proposed noise-shaping method employs lossless discrete integrators for realization in microwave and mm-wave array processing systems. The paper shows that 2-D noise-shaping reduces the spectral overlap of a desired array signal with that of noise. By reducing the overlap of the ROSs, 2-D filtering can be used to improve the overall noise figure of the array receiver. A noise figure improvement of 2.6 dB could be simulated for a 4-times spatially over-sampled array with 65 simulated elements for an input signal to noise ratio of 10 dB and LNA noise figure of 5 dB. Simulation results based on wideband signals on 33, 65, 129 and 257 element antenna arrays with 2, 4 and 8 times oversampling show the potential capability of the proposed system in improving overall noise figure. Although mathematical modeling shows potential improvements in receiver noise figure, RF integrated circuit realizations are challenging and have not been attempted yet.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".