Effect of Channel Estimation Errors on Turbo Equalization in ST Block Coded CDMA Downlink
Bibliographic record
Abstract
Space-time (ST) block coding based transmit diversity is considered in the third generation mobile communication standards for wideband-CDMA downlink transmission. On the other hand, turbo equalization can be used to enhance the wireless system performance. Hence, recently we proposed a minimum mean-square error (MMSE)-based turbo equalization scheme for the ST block coded CDMA downlink. In the proposed scheme, we assumed that perfect channel information is available at the receiver. However, in practice, channel coefficients are unknown to the receiver and have to be obtained through channel estimation. In this paper, we investigate the effect of the channel estimation errors on the MMSE-based turbo equalization in the downlink of ST block coded CDMA systems. It is demonstrated through simulations that substantial performance improvements can be obtained using the MMSE-based turbo equalization for moderate-to-high channel estimation quality
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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 | 0.015 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".