Node selection for sidelobe control in collaborative beamforming for wireless sensor networks
Bibliographic record
Abstract
Collaborative beamforming (CB) is a new technique for energy-efficient long-distance communications in wireless sensor networks (WSNs). It is based on the fact that the distributed nodes of a WSN can synchronize their carrier phases to form a beampattern with a stable mainlobe (independent on the node locations). However, sidelobes of such beampattern are found to be severely dependent on the particular node locations. High level sidelobes can cause unacceptable interference to unintended base stations and access points (BSs/APs). Therefore, controlling the sidelobes of CB has the potential to increase the network capacity and wireless channel availability. In this paper, we propose node selection for CB sidelobe control. A selection algorithm with low implementation complexity is developed to search over different node combinations. It aims at minimizing the interference at unintended BSs/APs. The performance of the proposed algorithm is analyzed in terms of the average number of trials and the achieved interference suppression. Simulation results match the analytical approximations and show the effectiveness of node selection for limiting the interference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".