Training Power Optimization for Amplify-and-Forward Cooperative Systems
Bibliographic record
Abstract
Cooperative communication techniques promise the advantages of MIMO (multi-input multi-output) communications for wireless scenarios with single-antenna terminals. A main assumption in majority of the existing literature on cooperative communications is the availability of channel state information at the receiver. In practice, knowledge of the channel is obtained by sending known training (pilot) symbols to the receiver. In this paper, we study the effect of training on the system performance for an amplify-and-forward relaying cooperative system with pilot-assisted channel estimator over quasi-static Rayleigh fading channels. Considering average received signal- to-noise ratio at the destination node as the objective function, we formulate an optimization problem for a single-relay scenario to answer the following fundamental questions: 1) How should the overall transmit power be shared between training and data transmission periods?; 2) How should training power be allocated to broadcasting and relaying phases?; 3) How should data power be allocated to broadcasting and relaying phases? Our simulation results demonstrate that optimized scheme significantly outperforms the original scheme with equal power allocation. Depending on the relay location, performance gains up to 5.5 dB are reported.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".