Joint delay and direction of arrivals estimation in mobile communications
Bibliographic record
Abstract
In this paper, we present a novel and precise way of estimating the direction and delay of arrivals in multipath environment for channel estimation purposes. Recently, super-resolution methods have been widely used for high-resolution direction of arrival (DOA) or time difference of arrival (TDOA) estimation. The proposed algorithm, called JDTDOA, is applicable to space–time channel estimation for space–time processing systems that employ hybrid DOA/TDOA technology. The estimator is based on conventional MUSIC algorithm to find the DOA and uses a standard correlator along with spline interpolation to find the TDOA of each arrival. In the interest of estimating the channel’s characteristics, each direction must be associated with its proper delay of arrival. To achieve this, we suggest a very simple and optimum beamforming by performing maximum variance distortionless response applied to each DOA found. The output at each DOA beamforming process gives the recovered signal from the relevant direction. A correlation is then made between each recovered signals which can be interpolated by cubic spline. The peak in correlation figure indicates the specific delay between the signal arrivals coming from the two considered direction.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".