Dynamic Traffic Offloading and Transmit Point Muting for Energy and Cost Efficiency in Virtualized Radio Access Networks
Bibliographic record
Abstract
A virtualized radio access network (VRAN) is envisaged in next generation wireless networks. Therein, users experience a seamless ubiquitous service without cell-specific signaling through transparent grouping of densely deployed transmit points (TPs) and helping UEs. Aiming at reducing the Carbon footprint as well as the operational expenditure while maintaining users' QoS, we propose an energy/cost-aware dynamic wideband muting and traffic offloading scheme for the VRAN. The proposed scheme favors muting hypotheses with greater energy/cost savings from TPs with relatively light traffic loads. Such loads are opportunistically offloaded to adjacent TPs to improve their energy efficiency. This is achieved by employing a low-complexity joint wideband muting and multi-point scheduling algorithm optimizing a novel energy-aware utility. The utility accounts for the power consumption models of different TPs, the current cost per unit energy and the TP's predicted 'Soft Loading Ratio'. Operator controls the energy savings-performance tradeoff in individual network regions regardless of the topology. Simulation results show significant energy efficiency and system capacity gains.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".