Hybrid Cognitive-Radio NOMA with Blind Transmission Mode Identification and BER Constraints
Bibliographic record
Abstract
The synergy of nonorthogonal multiple access (NOMA) and cognitive radio (CR) can provide efficient spectrum utilization for future wireless networks and enable supporting heterogeneous quality of service (QoS) requirements. In this context, this article aims at evaluating the throughput of a downlink CR-NOMA network where the secondary user (SU) data is opportunistically multiplexed with the primary user (PU) data using power-domain NOMA. The data multiplexing process is constrained by the PU QoS requirements. The multiplexing process can be considered seamless with respect to the PU because its receiver design will generally remain unchanged. Moreover, we consider the case where the SU detects its own data by blindly identifying the adopted transmission mode (TM) at the base station, which can be PU orthogonal multiple access PU-OMA, SU-OMA, PU/SU-NOMA, and no transmission. Consequently, the network can be classified as a hybrid underlay-interweave. The detection process is considered blind because the SU does not receive side information about the adopted TM. The obtained analytical results corroborated by Monte Carlo simulation results show that the proposed CR-NOMA network can provide substantial throughput improvement over conventional NOMA networks, particularly at low signal-to-noise ratios (SNRs) because the unutilized PU spectrum can be used by the SU. Moreover, in good channel conditions the PU can tolerate some interference from the SU, which may improve the channel utilization significantly.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".