On linear processing for dual-hop multi-channel relaying
Bibliographic record
Abstract
In this paper, we consider the amplified-and-forward relaying in a multichannel system with linear processing capability at the relay. We propose an analytical approach to study the linear processing performance with an aim to maximize the end-to-end achievable rate, assuming equal power amplification at the relay. For the class of permutation matrices as the special case of linear processing, the problem reduces to finding the optimal channel pairing scheme that maps incoming channels to outgoing channels at the relay. The proposed unified approach allows us to obtain the corresponding optimal permutation for channel pairing, for either relaying with or without the direct path available. Particular to the case when the direct path is available, such optimal pairing strategy has not been shown before. We further demonstrate that the so obtained optimal permutation is in fact also optimal among all unitary matrices for achievable rate maximization, thus, establishing the optimality of channel pairing approach among unitary linear processing schemes. Simulation results are presented to demonstrate the achievable gain of optimal channel pairing compared with non-optimal linear processing and non-pairing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".