Base Station Sleeping Mechanism for Reduced Delay Using Traffic Load Prediction
Bibliographic record
Abstract
Small cells are expected to be densely deployed in future networks to improve spectral and energy efficiencies.Due to the their small coverage and fluctuating number of users, the On-Off mechanism in small cell base stations (SBSs) has to be dynamically adapted in order to reduce the total energy consumption.However, the time delay associated with the transition from the Off to ON states can degrade the network performance.In this paper, a traffic prediction algorithm is proposed to perform a proactive SBS activation by anticipating future workload in SBS clusters using information of distance and received signal power of associated users.The distance and power measurements are smoothed using Haar wavelet filters to get a better approximation of the cluster's traffic load.Each SBS operates in a self-organized manner in coordination with the neighbouring SBSs in that cluster wherein the information of associated users are exchanged among SBSs.The work aims to adaptively modify the On-Off mechanism while minimizing the time delay that is incurred from the wake-up process of SBSs.Simulation results show that the proposed algorithm can significantly reduce the delay with a slight increase in power consumption.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".