Active Uplink Sensing Beamformer Design via Bayesian Cramér-Rao Bound Dual Optimization
Bibliographic record
Abstract
This paper presents a novel optimization framework for solving active sensing problems in wireless communications, in which a base station equipped with massive multiple-input multiple-output (MIMO) and a limited number of radio-frequency chains aims to estimate the channel parameters of a sensing target. Specifically, the receive beamforming matrix at the BS is designed sequentially through optimizing the Bayesian Cramér-Rao bound (B-CRB) metric at each sensing stage, while satisfying a rank constraint and that the receive beamformers must be implementable by analog phase shifters. The proposed approach tackles this B-CRB minimization problem in the Lagrangian dual domain. This dual optimization approach has the advantage of reducing the dimension of the search space from the number of antenna elements to the number of channel parameters, which is typically much smaller for sparse mmWave channels. We propose efficient numerical methods for obtaining the primal solution from the dual and subsequentially setting the phase shifts in each active sensing stage based on this approach. Finally, we demonstrate the benefits of the proposed approach as compared to existing beamforming strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".