Excessively Long Channel Estimation for CDD OFDM Systems Using Superimposed Pilots
Bibliographic record
Abstract
To fully exploit frequency diversity in cyclic delay diversity orthogonal frequency division multiplexing (CDD-OFDM) system, accurate channel estimation is crucial. Due to the excessive channel delay spread in CDD, traditional in-band pilot assisted channel estimation with limited frequency resolution fails to track the considerable variation in the frequency domain. Alternatively, increasing pilot overhead will degrade the system throughput significantly. In this paper, we propose to use superimposed pilots to estimate highly frequency selective channels in CDD-OFDM systems. Compared to in-band pilot based channel estimation, the proposed scheme with pilot symbols superimposed over each subcarrier has full frequency resolution, hence it is more robust to severe frequency selectivity from the excessively long CDD channel. Expectation-Maximization (EM) algorithm is employed to estimate the channel iteratively based on superimposed pilots and tentative soft decisions. At the end of each iteration, to exploit the inherent channel sparsity and refine the estimate, channel taps are sorted and selected according to power. Simulation results show that the performance of the proposed scheme is promising in time varying fading channels without an increase in pilot overhead.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".