Performance Analysis of Opportunistic Scheduling in Dual-Hop Multiuser Underlay Cognitive Network in the Presence of Cochannel Interference
Bibliographic record
Abstract
In this paper, the performance of a dual-hop multiuser underlay cognitive network is thoroughly investigated by using a decode-and-forward (DF) protocol at the relay node and employing opportunistic scheduling at the destination users. A practical scenario where cochannel interference signals are present in the system is considered for the investigation. Considering that transmissions are performed over nonidentical Rayleigh fading channels, first, the exact signal-to-interference-plus-noise ratio (SINR) of the network is formulated. Then, the exact equivalent cumulative distribution function (cdf) and the outage probability of the system SINR are derived. An efficient tight approximation is proposed for the per-hop cdfs, and based on this, the closed-form expressions for the error probability and the ergodic capacity are derived. Furthermore, an asymptotic expression for the cdf of the instantaneous SINR is derived, and a simple and general asymptotic expression for the error probability is presented and discussed. Moreover, adaptive power allocation under the total-transmit-power constraint is studied to minimize the asymptotic average error probability. As expected, the results show that optimum power allocation improves the system performance compared with uniform power allocation. Finally, the theoretical analysis is validated by presenting various numerical results and Monte Carlo simulations.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".