Approximating Ergodic Mutual Information for Mixture Gamma Fading Channels With Discrete Inputs
Bibliographic record
Abstract
To evaluate the performance of wireless multi-path channels with discrete inputs, this letter investigates the ergodic mutual information (EMI) of a generalized fading type named mixture gamma distribution (MGD) under M-ary quadrature amplitude modulation (M-QAM) signaling. An accurate closed-form expression for the EMI is developed to approximate the exact value. The MGD fading can be specialized into several regularly used fading cases including Rayleigh fading, Nakagami-m fading, η - μ fading, κ - μ fading, and KGfading, and then the corresponding closed-form EMI formulas are separately derived to measure these fadings. Compared with the Gaussian source signals based research on MGD fading, this work sheds light on the performance with discrete inputs instead of Gaussian inputs, thereby being more practical. This study can be regarded as a unified tool to evaluate the EMI with discrete inputs over arbitrary wireless channels.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| 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".