Performance analysis of MRC-chirp system over independent and correlated fading channels
Bibliographic record
Abstract
In this paper, chirp modulation is proposed to employ in Maximal Ratio Combining (MRC) diversity, referred to as MRCChirp system. Moment generating function (MGF) approach is used to derive easy-to-compute expressions for average bit error probability (ABEP) for two fading situations. Firstly, independent fadings with Rayleigh and Nakagami-m statistics are considered. Next, an exponentially correlated fading environment with Nakagami-m statistics is considered. The ABEP performance of the proposed system is illustrated using analytical expressions and using extensive Monte Carlo simulations. Numerical results show close agreement of analytical work with those of simulations. A discussion of numerical results on the performance of MRC-Chirp system as a function of diversity order L, chirp modulation parameters, and fading parameters is presented.
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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".