Tracking Cell Phones for 5G mmWave Beam Placement Using Advanced Kalman Filtering
Bibliographic record
Abstract
Achieving more reliable communication necessitates precise beam management, a process often challenged by the dynamic and unpredictable nature of mobile environments. This study proposes an advanced prediction methodology that integrates a beam sweeping technique to acquire user prototype trajectory information. The method leverages received signal strength (RSS) and interference signal to noise ratio (SINR) metrics collected over a defined period to construct a prototype trajectory that reflects the behavior of user equipment (UE) over time. To enhance the accuracy of user movement predictions, two distinct Kalman filter approaches-the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are applied to the prototype trajectory and data from proximity sensors. These filters are used to predict the user’s position and derive an average path representing the movement from one point to another. This comparative analysis enables the evaluation of prediction accuracy and its implications for adaptive beam steering in future. The findings demonstrate the potential of this approach that enable us to dynamically direct beams toward the user, thereby enhancing connectivity and overall network performance in mmWave-based systems. Such advancements are instrumental in addressing the challenges posed by high mobility and intermittent connectivity in nextgeneration wireless communication networks.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".