Statistical analysis of cascaded Nakagami-m fading channels with generalized correlation
Bibliographic record
Abstract
This paper studies the statistical analysis of cascaded Nakagami-m fading channels that are arbitrarily correlated and not necessarily identically distributed. The probability density function (PDF), cumulative distribution function (CDF), and the nth moment for the product of N correlated Nakagami-m random variables (RVs) are derived and presented in exact form expressions using the Meijer's G function. The cascaded channels are assumed to have flat and slow fading with arbitrarily non-identical fading severity parameters. Using these results, the impact of channel correlation and fading severity parameters are investigated for the cascaded Nakagami-m channels. Furthermore, performance analysis addressed by outage probability (OP), average channel capacity, and average bit error probability (BEP) for coherently detected binary PSK and FSK signals are derived. As a consequence of the versatility of Nakagami-m distribution, the derived expressions can compromise the statistics of other useful multivariate distributions such as One-sided Gaussian distribution with m = 1/2 and Rayleigh distribution with m = 1. To the best of the authors' knowledge, the derived expressions are novel and have not been reported in the literature. To aid and verify the theoretical analysis, numerical results authenticated by Monte Carlo simulation are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".