Sparse channel estimation and tracking for cyclic delay diversity orthogonal frequency division multiplexing systems
Bibliographic record
Abstract
ABSTRACT In cyclic delay diversity orthogonal frequency division multiplexing systems, the excessive channel delay spread and corresponding high frequency selectivity makes channel estimation a challenging task. In this paper, we propose a two‐stage scheme to estimate and track the highly frequency selective channel. At the preamble reception stage, least squares channel estimation withL0norm regularization is proposed to exploit the channel sparsity. At the data demodulation stage, an expectation–maximization algorithm with the most significant tap selection is developed to track channel variations by using the channel order obtained from the first stage. Compared with other estimation methods, the proposed scheme requires no prerequisite knowledge of delay parameter settings, which leads to more flexibility. Furthermore, the scheme can exploit the channel sparse structure by detecting the nonzero taps and, consequently, has better mean squared error performance. Simulation results show that the proposed estimation scheme can retain the provided diversity gain of cyclic delay diversity effectively in time‐varying fading channels. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".