Dual-Hop Robust Distributed Collaborative Beamforming Over Nominally Rectangular WSNs in Slightly to Moderately Scattered Environments : (Invited Paper)
Bibliographic record
Abstract
propose a new distributed collaborative beam-forming (DCB) solution that is robust (i.e., RDCB) against major channel estimation impairments over dual-hop transmissions through a wireless sensor network (WSN) of K nodes with a nominally deterministic geometry, presumably rectangular (or a fortiori square). The source S first sends its signal to the WSN. Then, each node forwards its received signal to the destination D after multiplying it by a properly selected beamforming weight. The latter aims to minimize the received noise power while maintaining the desired one equal to unity at the destination D. These weights depend on some channel state information (CSI) parameters. Hence, they have to be estimated locally at each node or fed back to it; resulting in either case in channel parameter estimation or feedback errors that could severely hinder DCB performance. Due to lack of space, we only account here for nodes location placement errors which would always amount to some additional phase error. Accounting for the massive connectivity characterizing new 5G and future 5G+/6G wireless technologies and the Internet of things (IoT), we develop alternative RDCB solutions that properly adapt both to monochromatic [i.e., line-of-sight (LoS)] and bichromatic (i.e., slightly to moderately scattered) propagation scenarios over the first hop while always assuming a LoS link over the second; referred to hereafter as MM-RDCB and BM-RDCB, respectively. We do so by exploiting i) very efficient asymptotic approximations at large numbers K of the WSN nodes and ii) the nominal geometric symmetries of their deterministic (rectangular or square) grid shapes. Furthermore, our new MM-RDCB and BM-RDCB solutions are distributed since their weights can be locally computed at every terminal, thereby dramatically improving both spectral and power efficiencies of the WSN. Simulation results show considerable gains and robustness of the proposed techniques in terms of achieved signal-to-noise ratio (SNR) against nodes location placement errors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".