Throughput and Outage Probability Analysis for Cognitive Radio-Non-Orthogonal Multiple Access in Uplink and Downlink Scenarios
Bibliographic record
Abstract
Non orthogonal multiple access (NOMA) in cognitive radio (CR) network has been recognized as potential solution to support the simultaneous transmission of both primary and secondary users. In addition, CR-NOMA can also be used to serve multiple secondary networks in overlay cognitive radio networks. The aim of our work is to increase the secondary user’s throughput without compromising in QoS requirements of the primary users. Our presented work analyses the performance of power domain NOMA in cognitive radio networks for both uplink and downlink scenarios. The primary aspect of the work is to investigate the impact of power allocation on spectrum efficiency and fairness performance of CR-NOMA. Objective function is to maximize the overall throughput under the QOS constraints of the users. We have derived closed form expressions for optimized power allocation coefficient(α) for CR-NOMA uplink and downlink communications. Parameters causing the channel outage, have been examined and conditions for outage probability is derived for CR-NOMA communication. Finally, we have presented the simulation results to validate the mathematical models that are developed for power allocation coefficient and outage probability.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".