Distributed Collaborative Beamforming Design for Maximized Throughput in Interfered and Scattered Environments
Bibliographic record
Abstract
In this paper, we consider a dual-hop communication from a source surrounded by M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</sub> interferences to a receiver, through a wireless network comprised of K independent terminals. In the first time slot, all sources send their signals to the network, whereas in the second time slot, the terminals multiply the received signal by their respective beamforming weights and forward the resulting signals to the receiver. We design these weights so as to minimize the interferences plus noises' powers while maintaining the received power from the source to a constant level. We show, however, that they are intractable in closed form due to the complexity of the polychromatic channels arising from the presence of scattering. By resorting to a two-ray channel approximation proved valid at relatively low angular spread (AS) values, we are able to derive the new optimum weights and prove that they could be locally computed at each terminal, thereby complying with the distributed feature of the network of interest. The so-obtained bichromatic distributed collaborative beamforming (B-DCB) is then analyzed and compared in performance to the monochromatic CB (MCB), whose design does not account for scattering, and the optimal CSI-based CB (OCB). Comparisons are made under both ideal and real-world conditions where we account for implementation errors and the overhead incurred by each CB solution. They reveal that the proposed B-DCB always outperforms MCB in practice; and that it approaches OCB in lightly to moderately scattered environments under ideal conditions and outperforms it under real-world conditions even in highly scattered environments. In such conditions, indeed, the B-DCB operational regions in terms of AS values over, which it is favored against OCB could reach until 50° and hence cover about the entire span of AS values.
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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 | 0.000 |
| 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".