Coordinated 3D spectrum utilization for B5G indoor HetNets: A collaborated crowdsensing approach
Bibliographic record
Abstract
Abstract The 5G and beyond (B5G) networks are expected to provide significantly increased capacity for diverse services with limited spectrum resources. However, new aspects of B5G networks particularly the ultra‐dense network deployment and the heterogeneous network structure, make spectrum resources highly distributed in three‐dimension (3D), which brings unprecedented challenges for highly efficient spectrum utilization, especially in an indoor environment. To tackle the challenges on dynamic spectrum utilization and improving the volume capacity of indoor 3D networks, a collaborated crowdsensing approach is proposed for the coordinated 3D spectrum utilization through integrating the crowdsensing, data analytics, and software defined network (SDN) techniques. The integration of sensing, learning, and intelligent control provides critical capabilities for timely observing 3D radio resources and enabling the coordinated radio resource utilization among co‐existing indoor heterogeneous networks (HetNets). Case study confirms the superiority of the proposed coordinating method on achieved volume capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".