SER of two-dimensional signalings in Rayleigh fading with channel estimation errors
Bibliographic record
Abstract
A general analytical framework for evaluating the performance of practical coherent two-dimensional (2-D) signaling in frequency flat Rayleigh fading with channel estimation is proposed in this paper. A new and simple analytical expression for the symbol error rate (SER) of an arbitrary 2-D constellation in Rayleigh fading in the presence of channel estimation errors is presented. This framework is applicable to many current channel estimation methods such as pilot symbol aided modulation and minimum mean square error estimation where the fading estimate is a complex Gaussian variable correlated with the channel fading. The sensitivity of various 2-D signaling formats to static and dynamic channel amplitude and phase estimation errors in Rayleigh fading can be easily studied using the derived formula. The new exact SER expression makes it possible to optimize constellation parameters and various parameters associated with channel estimation schemes. It also provides insights into choosing an appropriate signaling format for a fading environment with practical channel estimation methods used at the receiver.
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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 | 0.005 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".