Self-Organized Scheduling Request for Uplink 5G Networks: A D2D\n Clustering Approach
Bibliographic record
Abstract
In one of the several manifestations, the future cellular networks are\nrequired to accommodate a massive number of devices; several orders of\nmagnitude compared to today's networks. At the same time, the future cellular\nnetworks will have to fulfill stringent latency constraints. To that end, one\nproblem that is posed as a potential showstopper is extreme congestion for\nrequesting uplink scheduling over the physical random access channel (PRACH).\nIndeed, such congestion drags along scheduling delay problems. In this paper,\nthe use of self-organized device-to-device (D2D) clustering is advocated for\nmitigating PRACH congestion. To this end, the paper proposes two D2D clustering\nschemes, namely; Random-Based Clustering (RBC) and Channel-Gain-Based\nClustering (CGBC). Accordingly, this paper sheds light on random access within\nthe proposed D2D clustering schemes and presents a case study based on a\nstochastic geometry framework. For the sake of objective evaluation, the D2D\nclustering is benchmarked by the conventional scheduling request procedure.\nAccordingly, the paper offers insights into useful scenarios that minimize the\nscheduling delay for each clustering scheme. Finally, the paper discusses the\nimplementation algorithm and some potential implementation issues and remedies.\n
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".