HARQ-Based Grant-Free NOMA for mMTC Uplink
Bibliographic record
Abstract
Massive machine-type communication (mMTC) is one of the most rewarding and at the same time challenging components in the fifth-generation (5G) cellular solutions supporting the Internet of Things (IoT). The 5G mMTC is considering the use of a combination of two key mMTC enabling technologies-grant-free (GF) transmission and nonorthogonal multiple-access (NOMA), called GF-NOMA, which can potentially exploit the advantages of both schemes. A primary challenge in GF-NOMA is to reduce the packet drop rate. Owing to the decentralized nature of the GF schemes and the lack of control over user equipment, only hybrid automatic repeat request (HARQ) Type I has been employed for enhancing the reliability of GF-NOMA so far. In this article, uplink GF-NOMA transmission schemes using HARQ Type III are proposed. Two types of packet combining-1) chase combining and 2) incremental redundancy combining are considered. Moreover, we introduce a GF single-transmission (GFST) scheme where all redundancy versions of the packet are transmitted in one shot. We present a comprehensive evaluation of both the GF and the conventional grant-based methods in mMTC scenarios and demonstrate the superiority of our proposed methods over the existing ones.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".