Transmit power allocation for asymmetric bi‐directional relay networks using channel statistics
Bibliographic record
Abstract
This study proposes a transmit power allocation (TPA) scheme for bi‐directional relay networks with an objective of minimising the total power consumption to meet both the service quality (i.e. the outage probability) and individual power requirements. This new scheme focuses on the amplify‐and‐forward protocol‐based multiple‐access broadcast mode with asymmetric network traffics where the bi‐directional relay channel (BDRC) statistics are assumed to be available at the transmitters. A two‐step method is devised to solve the optimisation problem pertaining to the total power minimisation. In the proposed method, the system outage probability is first minimised subject to both the individual and total power constraints, and then the total power consumption of the network is minimised subject to the given service quality constraint based on the preliminary solutions achieved in the first step. This two‐step optimisation mechanism leads to a novel TPA algorithm for the relay and two sources of the network. Simulation results are provided to validate the proposed algorithm, showing that the proposed new power allocation scheme can significantly reduce the total power consumption, especially when the BDRC or the network traffic is asymmetric.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".