Joint Channel Estimation and Robust Beamforming Design for AF Relaying Using IMM Kalman Filters
Bibliographic record
Abstract
This paper addresses the joint problem of recursive channel estimation and robust beamformer design in peer-to-peer communication through a network of relays over time-varying radio channels. Using observed signal samples at the relay and receiver nodes, the Channel State Information (CSI) is estimated centrally by taking advantage of a Markov model for the transmitter-relay and relay-receiver channels, and employing either the Extended Kalman Filter (EKF) or the Cubature Kalman Filter (CKF). Based on the estimated CSI, two robust approaches are conceived for designing the relay beamforming where the aim is to minimize the total transmission power of the relays subject to Signal-to-Interference plus Noise Ratio (SINR) constraints at each of the receiver nodes. Furthermore, the Interacting Multiple Model (IMM) approach for mixing non-stationary and stationary Markov models is employed to extend the time-varying robust beamforming design to non-stationary environments. Through numerical simulations, the recursive CSI estimation methods are shown to be efficient, i.e., unbiased and converging to the Cramer-Rao Lower Bound (CRLB). Furthermore, the results confirm the better performance of the proposed robust relay beamforming design algorithms compared to existing methods in terms of relevant transmission metrics, including relay power consumption and spectral efficiency.
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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 | 0.004 |
| 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) | 0.002 | 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".