Sparse channel estimation using orthogonal matching pursuit algorithm
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Bibliographic record
Abstract
Sparse channels are encountered in several communication applications. Exploiting the sparsity, a channel estimate can be obtained by using a matching pursuit (MP) algorithm. Previously, it was demonstrated that the MP based channel estimation outperforms the conventional least squares (LS) estimation algorithm for sparse channels. In this paper, we propose to use the orthogonal matching pursuit (OMP) algorithm for channel estimation. Using OMP, the convergence problem in MP algorithm based on re-selection of the basis vectors is eliminated. It is also verified that by avoiding the re-selection problem more accurate channel estimates can he obtained by using the OMP algorithm. The performance of decision feedback equalizers based on the channel estimates obtained by using the MP and OMP algorithms are compared, verifying that the OMP outperforms the MP, with a comparable computational complexity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it