Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks
Bibliographic record
Abstract
The evolution towards 6G networks promises a massive increase in connected devices and demanding use cases, intensifying the challenge of managing limited spectrum resources efficiently. This paper addresses this challenge in an underlay cognitive radio network framework where secondary users (SUs) employ the code domain non-orthogonal multiple access (NOMA) mechanism for communication while incorporate energy harvesting (EH) to enhance their operational longevity and support green communication principles. Specifically, we assume SUs utilize EH via a wireless powered communication network (WPCN) process. A difficulty within this combined cognitive radio and WPCN scenario is the precise and efficient identification of active SUs for effective resource allocation and interference management. While traditional active user identification methods exist, they can face challenges, including computational complexity and experiencing limitations in accuracy under certain conditions. To address this issues we proposes the application of Grover’s quantum search technique. Furthermore, we investigate the impact of the number of users on the detection success probability and the trade-off between this probability and energy efficiency in this scenario. A comparison between the proposed approach and a non-quantum search technique is also provided.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".