A power-efficient method to increase common rate in AF multi-way relay channels
Bibliographic record
Abstract
Multi-way relaying is a cooperative scheme for communication scenarios where several users want to share their data with each other. While the capacity of multi-way relay channels (MWRCs) is still unknown to completely understand their potentials, the achievable data rates of MWRCs have been studied for specific setups. In this work, we aim to get a better understanding of the achievable data rates of an amplify-and-forward (AF) MWRC. To this end, we first present an achievable upper bound for the common data rate of an AF MWRC and discuss how users' power allocation affects the common rate. While full power transmission at the users assures reaching the maximum achievable common rate in one-way relaying, we show that it is not the case for MWRCs. Thus, we formulate an optimization problem to find the optimal users' power allocation to gain the maximum common rate. Finding an optimal solution for this optimization problem is quite complex since the number of constraints grows exponentially with the number of users. We then focus on a relaxed version of the rate optimization problem and propose a suboptimal algorithm to solve it. Our simulation results show that the proposed algorithm can achieve rates higher than the ones achieved through full power transmission with noticeably lower transmit power at the users.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".