Optimisation of power allocation for asymmetric relay placement in multi‐hop relay systems
Bibliographic record
Abstract
In this study, schemes for optimisation of power allocation (OPA) for asymmetric relay placement are presented for multi‐hop communication in a Rayleigh‐fading environment. For a decode‐and‐forward (DF) multi‐hop communication system, expressions are derived for optimised power allocation based on symbol error probability (SEP) and global channel state information (GCSI). The analysis for OPA based on GCSI is extended to a hybrid combination of amplify‐and‐forward (AF) and DF relays. Analysis is done for two kinds of modulation schemes: M ‐ary phase‐shift keying with coherent detection and orthogonal M ‐ary frequency‐shift keying with non‐coherent detection. Simulation results show that for a multi‐hop system with asymmetric relay placement, power optimisation schemes perform better than the conventional equal power allocation scheme. In addition, power optimisation based on GCSI shows substantially improved performance compared with power allocation based on end‐to‐end SEP. Further, performance comparison is shown for increase in number of relay nodes in an AF and DF multi‐hop system with and without power allocation. The performance of a DF system improves with increase in number of relay nodes whereas performance of an AF system degrades. Hybrid relaying provides an option to exercise switching between DF and AF so as to extract the maximum advantage of the two relaying schemes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".