Mean angle of arrival, angular and Doppler spreads estimation in multiple‐input multiple‐output system
Bibliographic record
Abstract
In this paper, the authors propose a new method to simultaneously estimate the mean angle of arrival (AoA), the angular spread (AS) and the maximum Doppler spread (DS). They exploit the multiple‐input multiple‐output (MIMO) Rayleigh channel with uniform linear arrays at both the transmitter and the receiver. They also consider the Gaussian and the Laplacian angular distributions for the incoming AoAs. The proposed method uses the first and the second derivatives of the received signals cross‐correlation functions. They take as benchmarks two estimators from the literature for the three parameters estimates. The spread root multiple signal classification (MUSIC) (SRM) estimator is used for the mean AoA and the AS parameters, whereas the auto‐correlation function (ACF)‐based approach is considered for the maximum DS estimates. These methods were developed for single‐input multiple‐output and single‐input single‐output systems. In this paper, the authors extend these algorithms to a MIMO configuration. Simulation results show that their algorithm outperforms the SRM one for the mean AoA and the AS estimation. For the maximum DS estimation, their approach offers lower error rate than the ACF‐based one when the AS and the mean AoA are small. For higher values of the couple AS and mean AoA, their algorithm presents similar results.
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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) | 0.001 | 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 | 0.001 | 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".