Beam tracking in phased array antenna based on the trajectory classification
Bibliographic record
Abstract
Abstract One of the major new concepts in 5G cellular communications is a shift in the way services are delivered to users. In the new 5G protocol, individual users are recognized and tracked through beams, which are targeted and specific to individual users. The millimeter‐wave (mmWave) communications impose a directionality which results in a significant challenge in serving mobile terminals and unmanned aerial vehicles. This challenge can be relieved in mmWave systems using analog beamforming. Based on the identification of the patterns in the reference signal received power (RSRP) measurements, some classifications are employed. Some trajectories are defined for different users in Hallways. Therefore, the angle of arrival (AoA) would be the same for the users following the same trajectory. The users based on the received RSRP values are clustered by K‐means and I‐Kmeans algorithms. To this end, our algorithms to find the beamforming coefficient are employed for only one user in each cluster and the complexity of the algorithms can be lower. The proposed algorithms are spatial frequency‐based beam tracking and angular domain beam tracking algorithms. Simulation results show that tracking the sine function of AoA achieves better performance compared to tracking the AoA. Moreover, the optimal number of clusters is obtained by using the Elbow method.
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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 | 0.001 |
| 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 | 0.001 |
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".