Analyzing Multi-Antenna Wireless Systems: Distribution Functions, Integral Identities, and Performance Metrics
Bibliographic record
Abstract
This work develops a novel framework for the performance evaluation of communication systems with receive diversity, in which the probability density function (PDF) statistic is formulated as a combination of power, exponential, and confluent hypergeometric functions. First, we establish the distribution function of the signal-to-noise ratio in the context where the receiver implements maximal ratio combining. Then, we propose closed-form solutions for integral identities involving this PDF and different functions, namely, algebraic, exponential, complementary error, generalized Q, and logarithmic. We further illustrate the suitability of the proposed formulation for different practical channel models, namely, shadowed κ-μ, extended ημ, and shadowed Beaulieu-Xie. In this context, we also derive various physical and data-link layer metrics, such as outage probability, average symbol error probability, optimal rate adaptation capacity, channel inversion with fixed rate capacity, and effective capacity, considering these practical fading models and different types of additive noises, including generalized Gaussian noise, Gaussian noise, Laplacian noise, and Gamma noise. Asymptotic analysis of the said metrics and of the diversity order of the system is also presented. The analytical results are validated using simulation results for various scenarios relevant to realworld applications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".