Distributed processing techniques for beamforming in wireless sensor networks
Bibliographic record
Abstract
A main task in distributed beamforming (DBF) techniques for wireless sensor networks (WSNs) is to maximize the received signal power at the access point (AP) while inflicting small interfering effect on unintended receivers. When the DBF nodes are unaware of the directions of unintended receivers, interference at the latter receivers may be substantially reduced by forming a beampattern with a narrow mainlobe that is pointed towards the AP. However, such an approach requires the DBF nodes to be sporadically scattered over a large area and, hence, increases the probability of the loss of their inter-connection. Assuming that nodes are uniformly distributed in the network, we show how the DBF nodes can be intelligently selected to ameliorate the network disconnectivity problem. In turn, when the directions of unintended receivers are known, one may aim to apply the so-called null-steering beamforming approach to effectively nullify the received power at those directions. However, it can be shown that implementing a null-steering beamformer in WSNs requires each node to be aware of the locations of all other nodes in the network; a requirement that opposes the distributed nature of WSNs. For such a scenario, we approximate the null-steering beamformer with another beamformer that is amenable to a distributed implementation.
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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".