An Integrated Discrete-Time Delay-Compensating Technique for Large-Array Beamformers
Bibliographic record
Abstract
This paper implements a wide aperture high-resolution true time delay for frequency-uniform beamforming gain in large-scale phased arrays. We propose a baseband discrete-time delay-compensating technique to augment the conventional phase-shift-based analog or hybrid beamformers. A generalized design methodology is first developed to compare delay-compensating analog or hybrid beamforming architecture with their digital counterpart for a given number of antenna elements, modulation bandwidth, ADC dynamic range, and delay resolution. This paper shows that delay-compensating analog or hybrid beamformers are more energy-efficient for high dynamic-range applications compared to true-time-delay digital beamformers. To demonstrate the feasibility of our proposed technique, a four-element analog delay-compensating baseband beamformer in 65-nm CMOS is prototyped. A time-interleaved switched-capacitor array implements the discrete-time delay-compensating beamformer with a wide delay range of 15-ns and 5-ps resolution. Measured power consumption is 47 mW with frequency-uniform array gain over 100-MHz modulated bandwidth, independent of angle of arrival. The proposed delay compensation scheme is scalable to accommodate the delay differences for large antenna arrays with higher range/resolution ENOB compared with prior art.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".