Power-Constrained Distributed Implementation of SNR-Optimal Collaborative Beamforming in Highly-Scattered Environments
Bibliographic record
Abstract
In this letter, we consider a power-constrained signal-to-noise ratio (SNR)-optimal collaborative beamformer (OCB) design in highly-scattered environments. We show that its weights depend on non-local CSI (NLCSI), thereby hampering its implementation in a distributed fashion. Exploiting the polychromatic (i.e., multi-ray) structure of scattered channels, we propose a novel distributed CB (DCB) design whose weights depends solely on local CSI (LCSI) and prove that it performs nearly as well as its NLCSI-based counterpart. Furthermore, we prove that the proposed LCSI-based DCB outperforms two other distributed-implementation benchmarks: the monochromatic (i.e., single-ray) DCB (M-DCB) whose design ignores the presence of scattering and the bichromatic (i.e., two-ray) DCB (B-DCB), which relies on an efficient polychromatic-channel approximation by two rays when the angular spread is relatively small.
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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 | 0.000 | 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".