Intracell Frequency Band Exiling for Green Wireless Networks: Implementation, Performance Metrics, and Use Cases
Bibliographic record
Abstract
The substantial increase in the number of base stations (BSs) compels researchers to focus on spectral efficiency (SE) and energy efficiency (EE) in wireless networks. To this end, we propose an intracell frequency band exiling (ICE) technique as a promising solution for green wireless networks. In the proposed technique, operating frequency bands of mobile users are assigned from upper frequency bands (UFBs) to lower frequency bands (LFBs) by suitably adjusting their coverage area to provide energy-efficient communications. To do this, we derive ICE probabilities on a log-normally distributed traffic model and calculate the EE and area SE (ASE) considering the power consumption model. The simulation results demonstrate that the ASE can be improved by increasing the traffic density. However, increasing the traffic density does not improve the EE beyond a certain threshold. Therefore, we present the tradeoff between EE and ASE and provide an optimum operating point. In addition, we show the ICE performance to be better than that of the existing cell-zooming (CZ) technique, unveil the ICE relation with beyond-5G (B5G) networks, and, finally, provide a cell-exiling manager system to illustrate the applicability of the proposed technique for various implementations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".