Statistical analysis of mobile radio reception: an extension of clarke's model
Bibliographic record
Abstract
Statistical analysis and modeling of wireless channels is essential to wireless communication systems. Clark's model [1] and the corresponding statistical analysis of mobile radio reception has been widely accepted in numerous wireless applications. Since the component phases in Clarke's model are assumed to be constant in time, the well-known results of statistical analysis based on this model, such as the autocorrelation and Doppler power spectrum, are not appropriate to describe real wireless channels for which the random environments (radio propagation paths) are time-varying and accordingly for which the channel is non-constant in the absence of Doppler frequency shift. In this paper, we extend the traditional Clarke's model incorporating the effect of fluctuations in the component phases, and perform the statistical analysis which results in a closed-form expression of the autocorrelation of the fading. The theoretical power spectral density function, which is the Fourier transform of the resultant autocorrelation of the fading, is shown to fit the practical measured spectra, which is in contrast to the traditional theoretical flat fading channel spectra (Jake's spectrum in [2]). The proposed model and statistical results should have important implications for detailed spectral analysis and channel simulations for real wireless communications systems in random fluctuating electromagnetic propagation environments.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".