Improved MC-CDMA reception employing two-dimensional decision-feedback pilot and data symbol correction
Bibliographic record
Abstract
An algorithm is presented that recovers, to a large degree, the performance degradation due to channel estimation and equalization errors in a multi-carrier code division multiple access (MC-CDMA) system. The algorithm uses the two-dimensional structure of the MC-CDMA frame to estimate the equalization error. It contains two steps that can be applied independently from each other. In the first step, the pilot symbols are corrected and channel estimation and equalization are redone for the received data symbol samples. In the second step, a possible remaining mismatch on the equalized data symbol samples is 're-equalized' and new symbol decisions on the 're-equalized' signal are made. It is shown that the algorithm is able to improve the performance in additive white Gaussian noise (AWGN) and fading channels with and without channel coding. On a frequency-selective fading channel and with standard R/sub cc/=1/2 convolutional coding, a performance improvement of approximately 4.75 dB is possible at a BER of 10/sup -6/.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".