Proactive Radio Resource Optimization With Margin Prediction: A Data Mining Approach
Bibliographic record
Abstract
Driven by the exponential surge on high data rate services, network operators are facing the challenges of how to enhance the capacity and optimize the coverage in a cost-efficient approach. However, traditional network optimization technologies passively adjust the network configurations based on network's congestion ratio, drop-off rate, coverage holes, etc., leading to suboptimum user experiences. Therefore, the objective of this paper is to optimize the network configurations by obtaining the accurate network status, user demand, and application request distribution based on the real-time data. The data mining technique is introduced to predict the resource margin based on historical measurement statistics. To explore the dynamic distribution of user demand and application request, a weighted k-nearest neighbors model is proposed to predict periodic characteristics of network traffics, denoting different temporal and spatial patterns of radio resource margins. In contrast to the traditional passive network optimization approaches, the radio resources can be reconfigured actively to meet the dynamic patterns of traffic loads by using the proposed optimization algorithm. Results prove that the proposed data mining model can capture the dynamics of traffic loads to optimize the traffic load balance and increase the efficiency of radio resource utilization using the network statistic data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".