Estimation of Fast-Fading Channels for Turbo Receivers With High-Order Modulation
Bibliographic record
Abstract
This paper proposes an efficient, low-complexity, and near-optimal approach to pilot-assisted fast-fading channel estimation for single-carrier modulation with a turbo equalizer and a decoder. The proposed method is applicable to higher order modulation schemes, where the detector is sensitive to the estimation error. A doubly selective fast-fading channel is estimated using a fixed-lag Kalman filter (KF). The Kalman filtering is followed by a zero-phase low-pass filter, functioning as a smoother. A method for designing the smoother is introduced. Block processing is utilized to reduce the transient effects of the zero-phase filter (ZPF) at the edges of the symbol blocks. A first-order autoregressive (AR) model is fitted to the channel variations. For the case of 64-quadrature-amplitude modulation (QAM), a complex-exponential basis expansion model (CE-BEM) is exploited to capture the varying gains, and an AR(1) process is used to model the time evolution of the BEM coefficients. By virtue of the long memory of the smoother, the error-rate floor, which is commonly associated with low-order channel estimation models, is avoided. The potential for parallelism makes our approach well suited to multicore field implementations. The applicability of the method to 4-QAM, 16-QAM, and 64-QAM schemes is shown through simulation experiments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".