Performance Analysis of Non-Orthogonal AF Relaying in Cognitive Radio Networks
Bibliographic record
Abstract
In this letter, we address the problem of maximizing the throughput of underlay cognitive networks, through optimal power allocation of non-orthogonal amplify-and-forward relays. The optimization problem is formulated and transformed to a quadratically constrained quadratic problem (QCQP). The optimal power allocation is obtained through an eigen-solution of a channel-dependent matrix where the corresponding signal-to-noise ratio (SNR) is shown to be the dominant eigenvalue of this matrix. Our optimal power allocation is shown to transform the transmission over the non-orthogonal relays into parallel channels, resulting in the received SNR to be the sum of the SNRs over the relaying channels. While closed-form expressions for statistics of the received SNR are mathematically intractable, we propose an approximation for the probability density function of the received SNR based on Gamma random distribution. The outage probability of the cognitive network is analyzed where the Gamma approximation is shown to be accurate and insightful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".