Enhancing the Performance of Amplify-and-Forward Cognitive Relay Networks: A Multiple-Relay Scenario
Bibliographic record
Abstract
In this paper, we address the problem of maximizing the received signal-to-noise ratio (SNR) of a relay-assisted secondary network. In particular, a pair of cognitive radio nodes communicate through a cluster of K non-orthogonal amplify-and-forward relays sharing the spectrum of a primary network in an underlay fashion. The interference at the primary receiver due to cognitive nodes transmissions must be below a tolerable level leaving the primary activity unaffected. We formulate an optimization problem to choose the transmission power of the secondary transmitter and the relays while adhering to the interference constraint on the primary network and imposing a maximum limitation upon the power consumption at every secondary node. While the optimization problem is nonconvex, we propose a simple iterative algorithm to achieve the solution. We present the performance of the proposed power allocation for different system parameters. Simulation results reveal a significant improvement of the achievable throughput of the proposed power allocation over equal power allocation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".