Channel estimation in bit-interleaved coded modulation with iterative decoding
Bibliographic record
Abstract
This study improves the fading channel estimation in bit-interleaved coded modulation systems with iterative decoding (BICM-ID) and signal space diversity (SSD) by embedding a training sequence. Existing training schemes work well at high signal-to-noise ratio (SNR) or slowly time-varying channels whereas the applications of BICM-ID are beneficial at low SNR and fast time-varying fading channels. Motivated by the power/bandwidth efficiency of the SSD technique and the fact that superimposed training outperforms pilot symbol-assisted modulation (PSAM) training over relatively fast time-varying channels, a new superimposed training sequence is explored. The proposed training sequence inserts pilot bits into the coded bits prior constellation mapping and signal rotation. This becomes a superimposed training sequence in the rotated symbols and helps the estimator to track fast variation of the channel gains. A soft iterative channel estimator is developed to work with the superimposed training sequence. The performance of the proposed scheme, namely SSD-pilot, is shown to be superior to PSAM scheme. To gauge the performance improvement achieved with the proposed channel estimation, an analytical bound on the asymptotic bit error probability for BICM-ID using SSD over correlated fading channels is provided. The Cramer–Rao bound on the mean-square error of the channel estimator is also derived to evaluate the performance of the iterative channel estimator.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 teacher head, 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".