Few-Shot Learning Based Hybrid Beamforming Under Birth-Death Process of Scattering Paths
Bibliographic record
Abstract
Data-driven methods provide an efficient solution for the implementation of hybrid beamforming. However, the current data-driven methods do not consider the birth-death process of scattering paths (BDPP) which is very common in the realistic scenario. When the BDPP happens, the statistical characteristics of channel state information change dramatically and suddenly. So, BDPP makes the design model need a lot of updates to adapt to the new channel environment. Therefore, the design model derived by current data-driven methods, which only consider the change of angle range, is not suitable under the BDPP. In this letter, inspired by the few-shot learning, we present the scheme of the weights initialization for the design model to address the problem of hybrid beamforming under BDPP. By the proposed scheme, when the number of paths changes, the design model with derived initial weights is able to quickly adapt to the new number of paths via a rather small number of on-line model updates only. Simulation results demonstrate the proposed method can offer a significant efficiency of adapting to the new number of paths over existing works.
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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 | 0.000 |
| 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".