Bidirectional AF Relaying With Underlay Spectrum Sharing in Cognitive Radio Networks
Bibliographic record
Abstract
This paper investigates the impact of primary transmissions on the performance of a dual-hop bidirectional cognitive radio system. The secondary users (SUs) communicate with each other in an underlay mode with the assistance of amplify-and-forward (AF) relays in the presence of primary users (PUs). Depending on whether there exists interference at the secondary transceivers, originating from the primary transmissions, or there is a possibility of employing the best relay selection technique at the secondary side, two operating cases are considered for the secondary network (SN). For both cases, tight lower bounds on the outage probability (OP) and the average symbol error probability (SEP) are derived in closed form. Moreover, a novel and general closed-form expression for the bit error rate (BER) of M-ary square quadrature amplitude modulation (QAM) for both single- and multiple-relay systems, under additive white Gaussian noise (AWGN) when Gray coded bit mapping is employed, is presented. In addition, we provide an upper bound and closed-form approximate expression for the ergodic capacity (EC). Numerical results provide important insights into the impact of system parameters on performance; for instance, it is demonstrated that, in both cases, under consideration of power constraints on the secondary nodes as dictated by the underlay mode of operation, the SN always achieves the full diversity order similar to the noncognitive counterpart.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".