Channel estimation and performance of BICM-ID with signal space diversity over time-correlated Rayleigh fading channels
Bibliographic record
Abstract
Signal space diversity (SSD) has been recently applied to improve the error performance of bit-interleaved coded modulation with iterative decoding (BICM-ID) systems over wireless fading channels. Previous works, however, only consider perfect channel estimation at the iterative receiver of the system. Performing channel estimation is an important task in a practical BICM-ID system and is the focus of this paper. To exploit the technique of SSD for improving channel estimation, a new method of using training sequences is presented. The proposed method inserts pilot bits between the coded bits, i.e., before constellation mapping and signal rotation. It is therefore can be considered as superimposed training. Developed is a soft iterative channel estimator to work with the new arrangement of the training signal. The performance of the system with the proposed training and channel estimation is shown to be superior that with pilot symbol assisted modulation (PSAM). To benchmark the achievable error performance of the system with channel estimation, an analytical bound of the asymptotic bit error probability for BICM-ID using SSD over correlated fading channels and perfect channel state information (CSI) is also derived.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".