Efficient and Accurate Semiblind Estimation of MIMO-OFDM Doubly-Selective Channels
Bibliographic record
Abstract
This paper proposes an efficient and accurate semiblind channel estimation technique for Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) turbo receivers. Once the channel is estimated using a few pilots, a low order Kalman filter is employed to progressively predict the channel gains for the upcoming blocks. A Basis Expansion Model (BEM) channel estimation scheme is used to allow the channel to vary within a block to make the method compatible with fast fading radio channels. As the detected data symbols are iteratively used by the Kalman filter to enhance the estimation accuracy, the proposed method compares with iterative pilot-aided systems in terms of computational cost and competes in spectral efficiency with semiblind and blind estimation techniques in fast fading environments. The BER performance of the proposed estimation approach is 0.3 dB off the perfect CSI case, whereas the computational complexity is on the order of that of near-optimal pilot-assisted methods.
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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 | 0.000 | 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".