Signal‐to‐noise ratio optimisation for multi‐input multi‐output relay systems with direct source–destination path
Bibliographic record
Abstract
The authors consider a relay communication system, where all nodes are equipped with multi‐input multi‐output antennas, and there is a direct path/channel between the source and the destination. Assuming a linear non‐regenerative relaying, the relay matrix is designed by maximising the signal‐to‐noise ratio of the system. The authors derive the optimum relaying matrix for different power constraints in the relay, including the fixed relay power constraint and constraint on the maximum transmitting power from the relay. Under the constraint of fixed total transmit power, they derive the optimum power budgets of the relay and source that only depend on four positive quantities. They prove that there always exists a rank‐one relaying matrix, which transmits signal in one‐dimensional subspace and leaves the other subspaces clean. In addition, when the quality of the source–destination (SD) channel is poor, this matrix is the best rank‐one relaying transform that maximises mutual information between the source and the destination. Finally, they conclude that the relaying is beneficial only if the link quality for the source–relay is twice better than that of the SD, where the link quality is proportional to the ratio of the channel power gain to the received noise variance.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".