Resource allocation in 5G heterogeneous networks with downlink‐uplink decoupled access
Bibliographic record
Abstract
Abstract Fifth‐generation (5G) heterogeneous network (HetNet) with downlink (DL) and uplink (UL) decoupled cell association strategy is a promising solution to challenges faced in fourth‐generation (4G) HetNet, ie, mitigating interference, addressing traffic imbalances, and enhanced sum‐rate. This work carries out performance analysis of 4G HetNet with DL and uplink coupled (DUCo) access scheme vs 5G HetNet with DL and UL decoupled (DUDe) access scheme employing outer approximation and heuristic algorithms. First, a mathematical model, mixed integer nonlinear programming (MINLP) problem, is formulated for DUCo access and DUDe access schemes considering cell association, addressing user traffic imbalances, mitigating interference, and sum‐rate maximization in HetNet. Then, the formulated problem is solved, employing outer approximation algorithm (OAA) to find near optimal solution. Similarly, heuristic algorithms are developed for DUCo access and DUDe access schemes considering cell association, addressing user traffic imbalances, mitigating interference, and sum‐rate maximization in HetNet. Detailed performance analysis of DUCo access and DUDe access schemes is done by comparing results employing OAA and heuristic algorithms. Simulation results have shown that proposed DUDe access scheme outperforms DUCo access scheme in HetNet in term of cell association, addressing user traffic imbalances, mitigating interference, and sum‐rate maximization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".