Data-Assisted Radio Resource Allocation in Shared Spectrum Multi-RAT Heterogeneous Network
Bibliographic record
Abstract
New Radio Unlicensed (NR-U) is the key representative access technology beyond 5G implementation to alleviate the spectrum crunch. NR-U shares a 5 GHz unlicensed band with WiFi, which has contention challenges for the coexisting systems due to physical and link layer protocols disparity. Being a scheduled access system, NR-U transmissions can only start at strict periodic time slots, which requires introducing a synchronization gap period in the listen-before-talk (LBT) approach. In this paper, we address these issues and analyze the impact of various gap-based NR-U approaches to the fair and efficient coexistence of the two networks. The dependency of successful spectrum access of the two systems on the gap period is also investigated. We also present a machine learning data-driven approach to unlicensed channel selection for spectrum sharing by NR-U. The results based on actual data collected from real-life WiFi deployment scenarios indicate significant improvement in coexistence performance and spectrum utilization of the unlicensed band with the proposed approach. It is shown through simulation results that the gap period before the backoff procedure provides better coexistence performance compared to the gap-based approach, where the synchronization gap is introduced after the LBT backoff. Further, the results indicate that if the gap interval exceeds a certain threshold value for each coexistence scenario, the WiFi network starts dominating the unlicensed channel, completely blocking the NR-U transmissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".