Switching rate of dual selection diversity in non-isotropic IID fading channels
Bibliographic record
Abstract
Switching rate plays an important role for system engineers in the design of selection diversity receivers. Switching antennas is similar to applying a step function to the receiver circuits, which causes transients in the receiver filters. These deleterious transients will corrupt channel estimation as well as creating “dead” time in the receiver filters. Recent work has studied the switching rates of selection diversity and hybrid selection diversity; however, these studies have examined exclusively isotropic scattering environments. No paper has been published on the switching rate of two-branch selection combining in a non-isotopic scattering scenario. The switching rate of a two-branch selection diversity combiner operating over multipath fading channels is studied. An analytical solution is derived for independent and identically distributed (i.i.d.) fading channels. Rayleigh and Rician cases with non-isotropic scattering are considered. Sample numerical results and discussion are presented. The results indicate that the switching rate is reduced in non-isotropic fading environments. Depending upon the characteristics of the non-isotropic environment, the switching rate may be reduced by as much as two to nine times relative to an isotropic environment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".