A New Turbo Equalizer Conditioned on Estimated Channel for MIMO MMSE Receiver
Bibliographic record
Abstract
In this letter, we propose a new equalization scheme based on minimum mean-square error (MMSE) criteria with a channel estimation error. Different from the existing work on linear MMSE receivers, we consider both the a priori information and channel estimation error in the equalizer design, and re-derive the soft output extrinsic information used for the channel decoder. Conditioned on the estimated channel, we developed the new autocorrelation and covariance matrix of the received vector, which are used to derive the equalizer coefficients. One of our major contributions of this letter is a general expression of the MMSE-based turbo equalization to account for both the channel estimation error and the a priori information, which is not presented in the literature. Simulation results show that significant performance improvement can be achieved with the proposed turbo equalizer.
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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 | 0.000 |
| 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".