Uplink Cluster-Based Radio Resource Scheduling for HetNet mMTC Scenarios
Bibliographic record
Abstract
Current telecommunication networks face a surge in the number of connected Machine-type Communication (MTC) devices, creating an unprecedented disproportionate demand for existing resources, especially when working with a Heterogeneous Networks (HetNets). This demand cannot be addressed adequately as the infrastructure's transition process between different generations is slow. Fourth Generation (4G) relies on Orthogonal Multiple Access (OMA), where a single user can occupy the same sub-channel, Orthogonality offers interference-free communication but for normal loaded scenarios, but under performs in overloaded scenarios. Whereas Fifth Generation (5G) is targeting more spectral efficiency by using the Non-orthogonal Multiple Access (NOMA), allowing MTC devices to share the same resources in frequency and time. However, NOMA medium access techniques in general have a complex scheduler design as group users/devices with aligned correlations. In this study, we formulate and simulate a 4G/5G Uplink scheduler that is based on dual NOMA-OMA. The objective is to achieve a tangible improvement in the spectral and scheduling efficiency of the network. We are able to optimize the system under HetNet objectives and clustering constraints in overloaded scenarios, to examine the limitations of both NOMA and OMA in overloaded scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".