Performance of power allocation schemes in a two-hop AF relay system with faded direct link
Bibliographic record
Abstract
Though power allocation has been studied extensively in the literature, as energy efficiency becomes more important in “green” wireless communication, we revisit to study the differences in SNR vs BER-based power allocation (PA) for relay communication when the direct link is severely faded. The average SNR and average BER expressions are first derived for an amplify-and-forward relaying protocol with Rayleigh fading channels. Based on the derived expressions at the destination through two-hop communication, closed-form expressions for optimum transmit power are obtained at the source and the relay. It is observed that the higher the total transmit power is, the bigger portion of it should be allocated to the relay independent of its location. For a given end-to-end BER, optimally selected relay's location is relatively more closer to the source if BER-based PA is employed than that of the SNR-based PA. It is also noted that the BER-based power allocation scheme can achieve 1dB performance improvement over the SNR-based counterpart when relay is centrally located and is in the higher transmit power regime resulting in energy saving, for a two-hop wireless system with no avail of direct communication between transceivers.
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 | 0.007 |
| 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".