Near-Optimal Channel Estimation for OFDM in Fast-Fading Channels
Bibliographic record
Abstract
In this paper, a low-complexity iterative receiver combining joint iterative channel estimation (CE) with symbol detection is proposed for coded orthogonal frequency-division multiplexing (OFDM) systems in fast-fading channels with Doppler frequencies up to 15% of the OFDM subcarrier spacing. The receiver exchanges information between the channel estimator and detector in an iterative fashion to obtain accurate estimates of the channel state information (CSI). The channel is modeled as a weighted sum of fixed basis expansion model (BEM) functions. The BEM coefficients are characterized as a multivariate autoregressive (AR) processes and estimated with a Kalman filter. The initial channel estimate is performed from sparse pilot signals. Data are detected and decoded, and the channel is estimated again based on the estimated transmitted data. The cycle of detection, decoding, and CE is repeated until convergence. It is shown that with a pilot-to-data ratio of 7/144 and with pilots consuming only 1/145 of transmission power, bit error rate (BER) performance within 0.1 dB of that achieved with perfect CSI is obtained for BER ≤ 10-5.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".