Cognitive Coded Cooperation in Underlay Spectrum-Sharing Networks Under Interference Power Constraints
Bibliographic record
Abstract
Since the radio-frequency spectrum is fast becoming scarce, increasing the spectral utilization is of utmost importance for the sustainable development of wireless communications systems. In an effort to improve the spectral efficiency, cooperative relaying techniques have recently been integrated into spectrum-sharing environments. In this paper, we examine the outage and bit error probabilities of dual-hop cognitive turbo-coded cooperative networks with outdated channel state information (CSI) subject to Rayleigh fading. We assume a spectrum-sharing environment where the transmit power conditions of the underlay network are governed by the combined power constraint of the interference in the primary network and the maximum allowable transmission power at the secondary network. In this network, cochannel interference from the primary transmitter on the secondary network is considered, and a single relay that maximizes the received signal-to-noise ratio (SNR) is selected among the secondary relays. To efficiently evaluate the key parameters on the system performance, we derive the analytical expressions of the end-to-end outage probability and bit error rate (BER) for the proposed scheme. Assuming binary phase-shift keying (BPSK), we obtain explicit upper bounds on the probability of bit error based on the pairwise error probability. Furthermore, we present simplified expressions of the outage probability in the high-SNR regime used to quantify the system performance in terms of diversity gain. Finally, simulation results are provided to verify the accuracy of our analytical framework.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".