Performance Analysis of Random Access NOMA for Critical mIoT With Timer-Power Back-Off Strategy
Bibliographic record
Abstract
Massive machine type communication (mMTC) and Internet-of-Things (IoT) networks provide global connectivity for massive number of end devices anytime-anywhere. The most challenging part in implementing such networks is the development of spectrum access strategies to provide ultra-reliable low-latency (URLL) transmissions for a large number of nodes with sporadic traffic behavior. Such networks have to deploy spectrum-efficient transmission schemes, and thus, non-orthogonal multiple-access (NOMA) is considered as a viable solution, that can be used to provide high number of URLL transmissions. We propose a random access NOMA transmission protocol (RA-NOMA) for IoT networks with large number of clustered IoT devices is proposed. The nodes in the proposed scheme adopt timer and power back-off strategies to transmit their short packets in a collision-free NOMA-based manner to achieve the URLL requirements. Closed-form expressions for network metrics, namely, delay violation probability, average packet latency, reliability, and effective sum rate (ESR) are analytically derived. Furthermore, the effect of blocklength, back-off timer (countdown value) and the number of active nodes on network metrics is explored. Additionally, the effect of the estimation error on the number of active nodes is analyzed and the impact of preamble transmit power on reliability in the presence of estimation error is investigated. Moreover, mathematical expressions for network metrics are also derived for NOMA-ALOHA with transmission diversity (NOMA-ALOHA-TD) in the underlying scenario, and the obtained results from NOMA-ALOHA-TD and the proposed RA-NOMA are compared.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".