Outage analysis of spectrum sharing multi-antenna multi-relay networks
Bibliographic record
Abstract
Prior results on performance analysis for cognitive relay networks mainly involve perfect channel state information (CSI), which is not readily available. Thus, the effects of outdated CSI on the performance of a multi-antenna multi-relay cognitive radio system are investigated in this work. To exploit the benefits of multiple antennas, collaborative zero-forcing beamforming at the secondary source is proposed to enhance the system performance under the outdated CSI. At the destination, the maximum ratio combining (MRC) diversity scheme is adopted. The Nth best relay selection strategy is applied before the secondary data transmission process. Closed-form expressions for the exact and asymptotic outage probabilities are derived for the secondary user over the Rayleigh fading in the first hop and Nakagami-m fading in the second hop with and without feedback delay. These analytical results can reveal the system diversity order and coding gain. Monte Carlo simulations are carried out to verify the correctness of our analysis. These new analytical results can reveal insights into the characteristics of the proposed system over the outdated fading channels, and can provide useful design criteria for relay-assisted spectrum sharing networks.
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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 | 0.008 |
| 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 | 0.001 |
| 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".