Compression-Aided Kalman Filter for recursive Bayesian estimation of sparse wideband channels in OFDM systems
Bibliographic record
Abstract
Many wideband wireless propagation media, including millimeter-wave and underwater acoustic channels, exhibit sparse impulse responses. Exploiting the sparse character of such channels using compressed sensing techniques can potentially lead to substantial savings in pilot overhead. In this paper, we address sequential Bayesian estimation of sparse/compressible channels with a Kalman tracking filter termed the “Compression-Aided Kalman Filter (C-A KF)”. As opposed to much of the previous work on Bayesian compressed sensing, our approach is not built on a hierarchical or hypothesis-testing method for the modeling of a priori information. Rather, we acquire a priori information directly from the propagation environment by employing header symbols in order to derive the sparsity pattern, i.e., support locations, and the dynamic evolution of the discrete-time channel taps. Based on a priori information extracted by the header symbols, we develop a state-space pair for the CA KF. Our experimental results suggest that the proposed C-A KF algorithm leads to significant reductions in pilot overhead, regardless of how rapidly the underlying channel conditions may change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".