Fast Grant Learning-Based Approach for Machine Type Communications with\n NOMA
Bibliographic record
Abstract
In this paper, we propose a non-orthogonal multiple access (NOMA)-based\ncommunication framework that allows machine type devices (MTDs) to access the\nnetwork while avoiding congestion. The proposed technique is a 2-step mechanism\nthat first employs fast uplink grant to schedule the devices without sending a\nrequest to the base station (BS). Secondly, NOMA pairing is employed in a\ndistributed manner to reduce signaling overhead. Due to the limited capability\nof information gathering at the BS in massive scenarios, learning techniques\nare best fit for such problems. Therefore, multi-arm bandit learning is adopted\nto schedule the fast grant MTDs. Then, constrained random NOMA pairing is\nproposed that assists in decoupling the two main challenges of fast uplink\ngrant schemes namely, active set prediction and optimal scheduling. Using NOMA,\nwe were able to significantly reduce the resource wastage due to prediction\nerrors. Additionally, the results show that the proposed scheme can easily\nattain the impractical optimal OMA performance, in terms of the achievable\nrewards, at an affordable complexity.\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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".