Device and Network Coordination for Opportunistic Utilization of Radio Resources in 3D Networks
Bibliographic record
Abstract
Device and network coordination is critical for efficient radio resource (RR) utilization while meeting Quality of Service (QoS) requirements in heavily congested future heterogeneous wireless networks featured with 3-Dimensional (3D) small cells (SCs). Device and network coordination assisted opportunistic and coordinated use of RRs in distinct bands could dramatically improve the spectrum utilization in these networks. In this study, overall communication performance enhancement through better utilization of opportunistically available spatially distributed RRs in a 3D SC is addressed considering two co-located networks operated in licensed band (LB) and unlicensed band (UB) while jointly accounting for several related factors like 3D spatial positions and QoS requirements of the devices. To confront this problem, a device and network coordination assisted solution is developed using Q-learning and Slotted-ALOHA principles. Then, to maintain performance standards, device and network coordination aided scheduling, power control and access prioritization schemes are discussed. Subsequently, regret based learning assisted algorithm is presented for the UB to optimally utilize RRs. In these solutions, both device-network and network-network interactions are considered. In results, approximately 75% better overall coordination efficiency over conventional methods is shown at the initial iterations for the scenarios with the highest device density demonstrating attractive performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".