Indoor Radio Dot Placement Optimization using UE Positioning and K-Means Clustering
Bibliographic record
Abstract
Indoor deployment with low cost and high capacity has shown to be a cost-effective solution in 5G wireless networks. In indoor 5G networks, Radio Dot (RD) units handle the wireless interfacing between UE devices and the core network. Strategic placement of indoor 5G RD units to ensure higher coverage of the space with optimal performance is challenging, since various factors could affect signal penetration, including floor plan, building materials, wall construction, frequency band, interference, dynamic factors like user density, etc. Most static parameters are well considered during the deployment stage, with deployment tools and network planning strategy. However, the dynamic impact of user density and distribution on channel quality and performance is still an open research area. With the user equipment (UE) positioning and channel quality indicator (CQI), the areas with poor channel quality data could be detected and further analyzed to dynamically determine RD locations and adjust accordingly for better network performance, without extra radio hardware costs introduced. This paper adopted the K-means clustering algorithm to evaluate the scenario where all the RD unit locations can be adjusted. Further, a Node Adjustment algorithm for RD units was proposed to improve indoor 5G network performance for a cost-efficient solution. The number of UEs and their distribution were simulated, and a comparative evaluation was conducted for different algorithms and various scenarios. The experimental results showed that considering dynamic information to adjust RD unit placements in a building could provide a cost-efficient solution to optimize indoor 5G network performance.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".