Direction of Arrival algorithms for user identification in cellular networks
Bibliographic record
Abstract
The Direction of Arrival (DOA) algorithms can estimate the incident angles of all the received signals impinging on the array. These algorithms give the DOAs of all the relevant signals of the user sources and interference sources. However, they are not capable of distinguish and identify which one is the direction of the desired user. In this paper, we propose to use a reference signal which is known by the transmitter and the receiver to identify which one of the estimated DOAs is the direction of the desired user in the cell. Using a reference signal and applying the correlation concept, the DOA algorithms can distinguish the desired signal from the others. Moreover, we implement the Affine Projection Algorithm (APA) to enhance the accuracy of the estimated direction and to form a beam towards the desired user and nulls towards the interferes. Our simulation results assure that, in the presence of the reference signal, the DOAs algorithms can identify the direction of the desired user with high accuracy and resolution. We have applied this concept to MUSIC, ROOT MUSIC, and ESPRIT algorithms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".