An SNR balancing approach to two-way relaying
Bibliographic record
Abstract
We consider a relay network which consists of two transceivers and r relay nodes. Assuming that the transceivers and the relays are all equipped with single antennas, we devise a two-way amplify-and-phase-adjust relaying scheme. In this scheme, each relay multiplies its received signal by a complex weight and transmits the so-obtained signal thereby participating in a distributed beamforming process. We deploy an SNR balancing technique where the smallest of the two transceiver SNRs is maximized while the total transmit power is kept below a certain power budget. We show that this problem has a unique solution which can be obtained through an iterative procedure with a linear computational complexity per iteration. We also prove that for any channel realization, this approach leads to a power allocation scheme where half of the maximum power budget is allocated to the two transceivers and the remaining half will be shared among all the relay nodes. We further devise a distributed implementation of our proposed scheme which requires a minimal cooperation among the two transceivers and the relays. In fact, we show that our technique can be implemented such that the bandwidth required to obtain the beamforming weights in a distributed manner remains constant as the size of the network grows.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".