Maximal ratio combining with channel estimation errors
Bibliographic record
Abstract
An ideal coherent maximal ratio (MR) combiner multiplies the signals on all the incoming channels by the complex conjugate of their complex channel gains and then sums the resultant products to provide the optimum combined signal. However, the channel gains can not be estimated perfectly in any practical system. Thus, system performance will be degraded because there will be an error in the estimation of the combiner weighting factors in realistic combiners. In the paper closed form results are obtained for the bit error rates (BER) of MFSK, MPSK (M=2,4,8) and MDPSK (M=2,4) using imperfect MR combining in Rayleigh fading. These results are used to approximate the BER performance of equal gain (EG) combining with imperfect co-phasing. The MFSK results are also compared with those of the optimum non-coherent square-law (SL) combiner to determine how good channel estimation must be to surpass the performance of the optimum non-coherent combiner.
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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".