Turbo Equalization for Alamouti Space-Time Block Coded Transmission
Bibliographic record
Abstract
Recently, space-time block coding (STBC) has received a remarkable interest as an effective transmit diversity technique to combat channel fading, but STBC provides little or no coding gain. However, as channel coding is usually employed in wireless systems, the combination of channel coding and STBC can be used to achieve high throughput gains over wireless channels. On the other hand, turbo (iterative) equalization can be employed in channel coded broadband wireless systems to further enhance the performance. Hence, in this paper, we propose a minimum mean square error (MMSE)-based turbo equalization scheme for Alamouti space-time (ST) block coded multiple-input multiple-output (MIMO) systems. In the proposed iterative receiver, widely linear (WL) processing is used to exploit the rotational variance of the ST block coded transmit signal. Equalization and ST block decoding are jointly carried out at each iteration using the a priori information delivered by the convolutional channel decoder from the previous iteration. The extrinsic information generated by the combined soft equalization-ST block decoding stage is passed to the channel decoder as the a priori information. The simulation results demonstrate that high performance improvement can be obtained using the proposed iterative scheme in comparison with thenon-iterative equalization. Due to the low-complexity, the proposed iterative scheme may be highly attractive to be implemented in future Alamouti ST block coded wireless systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".