NOMA-Based User Cooperation With Incremental Hybrid Forwarding Protocols
Bibliographic record
Abstract
In this paper, we investigate the performance of a non-orthogonal multiple access (NOMA) wireless system, in which signal transmission of a far user is assisted by a near user. In particular, we examine the outage performance of the underlying scenario with two cooperative forwarding protocols incorporated, namely, the hybrid decode-amplify-forward (HDAF) protocol and the incremental hybrid decode-amplify-forward (IHDAF) protocol. HDAF combines the advantages of decode-and-forward (DF) and amplify-and-forward (AF) and overcomes their limitations. On the other hand, the IHDAF protocol gives priority to successful decoding from the direct link before enabling HDAF. Specifically, we first derive outage probability expressions for the two users under the HDAF protocol. Furthermore, a closed-form outage expression of the weak user is derived considering the IHDAF protocol, which is envisioned to further boost the outage performance. Also, we present a comprehensive mathematical framework in order to evaluate the system throughput performance of the underlying system model, for the HDAF and IHDAF protocols. Analytical and simulation results reveal that the outage performance of the weak user under IHDAF outperforms that of HDAF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".