Trajectory-Based User Tracking and Beam Assignment in a Hallway using Phased Array Antenna
Bibliographic record
Abstract
In this study, we present a phased array antenna system for trajectory-based user tracking and beam assignment in a hallway. We suppose that the antenna array covers all three of the predetermined paths in the hallway and that there are three of them. To enhance communication quality and lessen interference, the system aims to identify which user is on which trajectory and assign a beam to each user. We combine a number of signal processing strategies, such as beamforming and user tracking algorithms, to accomplish this. While the user tracking algorithm uses data from antenna to estimate the location and movement of users within the hallway, the beamforming technique is used to create directional beams that are directed towards each trajectory. Our evaluation of the proposed system demonstrates its capability to precisely track users and allocate beams depending on their placement inside the predetermined trajectories. The findings show that when compared to current techniques, the suggested system can enhance signal quality and decrease interference. The system can assist in increasing the dependability and effectiveness of wireless communication in these contexts by precisely tracking users and allocating beams based on their location within the hallway.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".