Distributed Collaborative Beamforming in the Presence of Angular Scattering
Bibliographic record
Abstract
In this paper, a collaborative beamformer (CB) is considered to achieve a dual-hop communication from a source to a receiver, through a wireless network comprised of K independent terminals. Whereas previous works neglect the scattering effect to assume a plane-wave single-ray propagation channel termed here as monochromatic (with reference to its angular distribution), a multi-ray channel termed as polychromatic due to the presence of scattering is considered, thereby broadening the range of applications in real-world environments. Taking into account the scattering effects, the weights of the so-called polychromatic CB (P-CB) are designed so as to minimize the received noise power while maintaining the desired power equal to unity. Unfortunately, their derivation in closed-form is analytically intractable due to the complex nature of polychromatic channels. However, when the angular spread (AS) is relatively small to moderate, it is proven that a polychromatic channel may be properly approximated by two rays and hence considered as bichromatic. Exploiting this fact, we introduce a new bichromatic CB (B-CB) whose weights can be derived in closed-form and, further, accurately approximate the P-CB's weights. Yet these weights, which turn out to be locally uncomputable at every terminal, are unsuitable for a distributed implementation. In order to circumvent this shortcoming, we exploit the asymptotic expression at large K of the B-CB whose weights could be locally computed at every terminal and, further, well-approximate their original counterparts. The performances of the so-obtained bichromatic distributed CB (B-DCB) and its advantages against the monochromatic DCB (M-DCB), which is designed without accounting for scattering, are analytically proved and further verified by simulations at practical values of K.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".