Symbol error rate calculation and data pre‐distortion for 16‐QAM transmission over nonlinear memoryless satellite channels
Bibliographic record
Abstract
Abstract This paper presents a simplified mathematical approach to evaluate the performance of any given circular constellation of 16‐level quadrature amplitude modulation (16‐QAM) in terms of symbol error rate (SER). Following this approach, with the aim to work with memoryless nonlinear satellite channels, a model is derived as a generalized form for both linear and nonlinear channels in the presence of down link additive white Gaussian noise (AWGN). The analysis provides means to calculate the optimal ring ratio (RR) and phase difference (PD) for several possible candidates of 16‐QAM circular constellations. The effects of RR and PD on the SER performance are investigated in the analysis. To overcome the nonlinear distortion, data pre‐distortion is taken into account in the study. The paper gives a general procedure for data pre‐distortion implementation for all circular 16‐QAM constellations. The analytical formulation has been extended for total degradation (TD) performance measure as a function of input back‐off (IBO) of the nonlinear amplifier. A SER performance‐comparison between different constellations for 16‐QAM systems has also been presented in this paper. The analytical results are validated by simulation. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".