DoA Estimation in Hybrid Analog and Digital Receivers using Orthogonal Analog Combiners.
Bibliographic record
Abstract
We develop two novel algorithms for estimating the direction of arrival (DoA) of mul- tiple sources in a hybrid analog and digital (HAD) receiver with both fully-connected (FC) and partially connected (PC) architectures. In HAD receivers, analog combiners project the received signal on a particular subspace. There can be DoAs in which the received signals will be heavily attenuated or nullified by the analog combiner. That is, an analog combiner defines spatial sectors, beyond which DoAs are unde- tectable. The first algorithm uses one or more analog combiners, each spanning a distinct subspace and collectively spanning the entire space. A standard DoA es- timation technique is applied by the digital combiner to estimate the DoAs within each sector. The estimates of the first algorithm may not be sufficiently accurate for practical applications. To remedy this weakness, Algorithm 2 performs sequential estimation refinements by successively narrowing the window over which the search is performed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".