Distributed Beamforming for Wireless Sensor Networks in Local Scattering Environments
Bibliographic record
Abstract
In this paper, transmit and receive collaborative beamforming (CB) techniques are considered to achieve a dualhop communication from a source to a receiver, through a wireless sensor network (WSN). Whereas the previous works assumed a model of plane wavefronts, here, a local scattering in the source or receiver vicinity is considered, thereby broadening the range of applications in real-world environments. Taking into account the local scattering, these beamformers aim to maintain the beamforming response in the desired direction equal to unity. It is shown that the so-obtained beamformers are not suitable for a distributed implementation in WSNs. We hence propose a novel distributed collaborative beamforming (DCB)technique that can be implemented in a distributed fashion and, further, well-approximates both transmit and receive CB techniques. The performance of the proposed DCB is analyzed and its advantages against the conventional DCB, which is designed without taking into account the presence of local scattering in the source or receiver vicinity, are analytically proved and are further verified by simulations.
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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".