Joint Robust Relay Beamforming and Adaptive Channel Estimation using Cubature Kalman Filtering
Bibliographic record
Abstract
In this paper, an adaptive algorithm is proposed for the estimation and tracking of the channel coefficients in peer-to-peer communication through a network of relays. Using the observed signals at the relay and destination nodes, the channel state information (CSI) is estimated centrally by taking advantage of a Markov model for the source-relay and relay-destination channels, and employing the Cubature Kalman Filter (CKF). The estimated CSI is used for solving a robust relay beamforming problem, aiming to minimize the total transmitted power by the relays subject to signal-to-interference-plus-noise ratio (SINR) constraint at each one of the destination nodes. Through simulations, the proposed CSI estimation is shown to be unbiased and converge to the Cramer-Rao-Lower-Bound (CRLB) for low and moderate error levels. Furthermore, the ensuing beamformer design exhibits better performance compared to existing robust beamforming methods.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".