Cognitive NOMA With Blind Transmission-Mode Identification
Bibliographic record
Abstract
This work presents a novel nonorthogonal multiple access (NOMA) cognitive radio (CR) system where the base station (BS) opportunistically multiplexes the secondary user (SU) with the primary user (PU) using power-domain NOMA. As the PU has the priority to transmit and SU is satisfied on best-effort basis, four different transmission-modes (TMs) are produced at the BS, which are PU orthogonal multiple access (PU-OMA), SU-OMA, PU/SU-NOMA, and silent mode. Consequently, the considered protocol can be classified as a hybrid underlay-interweave CR-NOMA. The TM adaptation should be seamless for the PU where its detector configuration remains unchanged regardless of the active TM. In contrast, the SU has to identify the active TM blindly, i.e. without side information, to select the appropriate detector. The identification process is performed using a classifier that is designed based on the maximum likelihood criterion. The performance of the proposed system is analyzed in terms of throughput, packet error rate (PER), and classification error. The Binomial and Multinomial theorems are utilized to simplify and allow a tractable analysis. The derived closed-form expressions, corroborated by Monte-Carlo simulation results, show that the hybrid CR-NOMA can provide substantial throughput improvement over conventional NOMA, which is about a 100%.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".