Fairness Oriented H-NOMA Devices Association and Pairing in HCN Slicing Using Cooperation Games
Bibliographic record
Abstract
Device association (DA) and pairing (DP) are presented in this letter to evaluate the fairness of different services between enhanced mobile broadband devices (eMBBDs) and ultra-reliability low latency communications devices (uRLLCDs) in RAN-slicing, which has emerged as a viable technique for enhancing total network throughput in heterogeneous cellular networks (HCNs) design. To optimize the eMBBDs' downlink (DL) sum rate while fulfilling the provisions of the uRLLC traffic. A cooperative Nash bargaining solution (NBS) is formulated to depict the association method. First, base stations (BSs) create random pairings between heterogeneous non-orthogonal multi-access (H-NOMA) devices. The BSs are classified into coalitions through the Hungarian approach. So, a two-band partition algorithm is evolved for the two BSs in each coalition to negotiate their allocated devices to improve the NBS utility. A multiplayer bargaining approach is constructed using this algorithm and the Hungarian technique. After finishing the DA process, we study the cooperative matching algorithm to optimize the pairing problem between devices' slices. Simulation results demonstrate that comparing the proposed approach to other schemes can achieve a significant DL rate distribution for eMBBDs, device-side latency for uRLLCDs, and fairness improvements.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".