Performance of power allocation schemes in an amplify-and-forward single-relay system with diversity at destination
Bibliographic record
Abstract
Different strategies can be followed to achieve efficient power allocation in wireless systems. While optimising the average received SNR would directly result in an optimised average BER and outage probability in a direct wireless communication link, it is not clear whether we can achieve an optimised average BER and/or outage probability in a relaying system by optimising the average received SNR. In this paper, the problem of power allocation in a single-relay amplify-and-forward wireless system with maximal ratio combining (MRC) at the destination terminal is investigated via optimising three different objective functions: average SNR, average BER and outage probability. The average SNR, average BER and outage probability expressions are first derived as a function of source and relay transmit powers and variance of the channel gains. Based on these expressions, closed-form expressions are derived for optimum transmit power allocation between source and relay. It is observed that the BER-based and outage-based power allocation schemes achieve performance improvement over the SNR-based scheme when the relay is closer to the source; however, all the three schemes perform equally when the relay is close to the destination.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".