A Dual-Hop Amplify-and-Forward MIMO Cooperative Beamformer in Distributed Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we propose a cooperative beamforming (CB) technique for dual-hop amplify-and-forward communication in WSNs with one source, N - 1 interferences, and K relay nodes. First, an optimal beamformer is obtained that maximizes the signal-to-interference-plus-noise ratio at the intended receiver in the far-field subject to the following constraints: 1) The relay nodes total transmit power is bounded; and 2) the received power from the N transmitters at L - 1 unintended receivers in the far-field is zero. It is shown that the optimal beamformer can only be implemented if each relay node knows the locations and the backward channels of all other relay nodes in the network. As this knowledge is not typically available at nodes in WSNs, we use a technique to approximate the optimal beamforming coefficients with quantities that depend only on the locally-available information at each individual relay node. The average beampattern expression of the proposed CB technique is then derived and its properties are analyzed. In particular, it is shown that the average gain of the beamformer linearly increases with K in the direction of the intended receiver while remaining fixed in the directions of the unintended receivers.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".