Markov‐based analysis for cooperative <scp>HARQ</scp>‐aided <scp>NOMA</scp> transmission scheme in <scp>5G</scp> and beyond
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Bibliographic record
Abstract
Abstract In the fifth generation (5G) wireless communication system, the non‐orthogonal multiple access (NOMA) scheme has recently been proposed as a possible candidate for improving spectral efficiency. We look at power‐domain NOMA in this study, in which, multiple simultaneous transmissions are allowed at the same channel with different transmission powers. To achieve reliable transmissions in NOMA, the hybrid automatic repeat request (HARQ) scheme can be integrated into NOMA. The performance of a downlink NOMA system using the Type I HARQ method is investigated in this research. This type of HARQ does not entail expensive device which is considered one of the requirements for a massive 5G‐based Internet of Things network. Unfortunately, in the literature, it has been shown that deriving closed‐form expressions for the outage probabilities of HARQ‐aided NOMA systems is intractable due to the incorporation of multiple fractional random variables. Consequently, in this article, we follow a simple yet efficient methodology to study our considered HARQ‐aided NOMA system. The HARQ‐assisted NOMA system on the downlink is modeled as an absorbing discrete‐time Markov chain (DTMC). Thereafter, under perfect and imperfect channel estimation, we use this model to examine system performance in terms of outage probability and estimated number of retransmissions. Interestingly, we demonstrate that our DTMC‐based analysis is straightforward, repeatable, and accurate. Moreover, in this article, we propose a cooperative HARQ (CHARQ) scheme for the downlink NOMA system. According to CHARQ, the users cooperate with the base station in the retransmission process that will result in improving the performance of NOMA. Monte Carlo simulations have been conducted to not only verify the accuracy of our DTMC‐based analysis, but also manifest the superiority of CHARQ over HARQ.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it