Joint delay and direction of arrivals estimation in mobile communications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 it