A Stability-Guarantee Beamforming Scheme in Multi-Loop Wireless Control Systems
Bibliographic record
Abstract
In this paper, we propose a stability-guarantee beamforming (BF) scheme for a multi-loop wireless control system, where a multi-antenna controller adopts the receiving BF technique to concurrently manage multiple control loops operating on the same spectrum. Specifically, we first use the convergence rate to characterize the control performance, and combine it with the pre-defined Lyapunov-like function to derive the control stability condition. Then, in order to improve the control performance while preserving the stability, an optimization problem is formulated to minimize the maximum convergence rate among all control loops, subject to the transmit power budget of each control loop and control stability condition. Since the original problem is mathematically complicated, with the help of zero-forcing principle, we propose an iterative algorithm using Lyapunov stability theorem, exponential accumulation and sequential convex approximation to solve it efficiently. Finally, simulation results verify that the introduction of BF in our scheme can significantly improve the control stability and reduce the control cost compared with the existing scheduling-based control scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".