NOMA Based Matching Game Algorithm for Narrowband Internet of Things (NB-IoT) System
Bibliographic record
Abstract
Narrowband Internet of Things (NB-IoT) is introduced by the third generation partnership project (3GPP) as a standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can satisfy many IoT requirements, Nevertheless, NB-IoT suffers a low data rate and low network capacity. This paper provides nonorthogonal multiple access (NOMA) scheme based matching game for uplink in NB-IoT systems to enhance the capacity and data rate by providing more connectivity for massive MTC devices. We formulate our optimization problem to maximize the total system rate by using a matching game. Simulation results show that the proposed scheme increases the total system rate by at least 150% and the system capacity by at least 125%, compared to OMA, and NOMA-water filling scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".