On the performance of non-coherent transmission schemes with equal-gain combining in generalized Κ-fading
Bibliographic record
Abstract
The generalized κ-fading model, characterized by two parameters, κ and m, is a very versatile model and was recently shown to accurately capture the effects of composite shadowing and multipath fading in wireless communication systems. Furthermore, it can be used to model cascade multipath fading, which is relevant in, e.g., mobile-to-mobile communication scenarios. In this paper, we derive closed-form expressions for the bit error probability of two non-coherent transmission schemes over L diversity branches being subject to generalized κ-fading. Specifically, focus is on binary differential phase-shift keying (DPSK) and binary non-coherent frequency-shift keying (FSK) modulation with (post-detection) equal-gain combining at the receiver. We also discuss the extension of our results to M-ary modulation schemes. Considering both independent and correlated fading across the L branches, we derive expressions for the asymptotic diversity order, which reveal an interesting interplay between the two fading parameters κ and m. Moreover, we show that the diversity order of the considered non-coherent transmission schemes is the same as in the case of a coherent transmission scheme. Finally, numerical performance results are presented, and our analytical results are corroborated by means of Monte-Carlo simulations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".