Power-Aware Coexistence of Wi-Fi and LTE in the Unlicensed Band Using Time-Domain Virtualization
Bibliographic record
Abstract
The rapid increase of mobile devices requires more network capacity as limited licensed spectrum is diminishing access to long-term evolution (LTE) users. As increasing licensed spectrum becomes costly, a cost-effective solution that improves network capacity is needed. Another overlooked issue is spectrum efficiency, where unused resources could be allocated to more LTE users. This article proposes two solutions to address network capacity and spectrum efficiency: moving into unlicensed spectrum and implementing wireless resource virtualization. In addition, as these solutions enhance data rate, thus increasing power consumption, this article includes power-aware optimization as a solution. Operating in the unlicensed band poses a challenge as 5-GHz bands are predominately used by Wi-Fi systems. Based on this challenge, we are proposing a time-sharing wireless resource virtualization (WRV)-based LTE/Wi-Fi coexistence solution that considers joint resource/access point (AP) allocation and power control problems for both technologies. Accordingly, we formulate a mixed-integer nonlinear programming problem and solve using the Lagrangian dual decomposition. We also propose low-complexity heuristics to solve these problems. Results show that the coexistence of Wi-Fi and LTE can be achieved in the unlicensed band with improved power savings using virtualization. Moreover, there is little to no difference in power savings in heterogeneous-traffic scenarios as the algorithm prioritizes maximizing rate to minimizing power.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".