Modelling, measurement and analysis of narrowband fast fading on relay channels
Bibliographic record
Abstract
A procedure for the analysis of narrowband fast fading data with statistically non-stationary characteristics is proposed and applied to data obtained from wideband radio propagation experiments involving two-hop relay channels at 2.25 GHz. The reported experiments were conducted from two channel sounder base station sites in Ottawa to enable a study of the characteristics of relay links in urban microcell environments and of vehicle-to-vehicle communications in relay-based cellular networks. It is shown that empirically-derived cumulative probability distributions for narrowband fast fading on relay links during time intervals when such fading exhibits quasi-stationary characteristics conform closely to a Double-Rician model, the derivation of which is detailed. Analysis of the Double-Rician distribution shows that Double-Rician fading is deeper than Rician fading, unless the K-ratios of the Rician links comprising the relay link are highly dissimilar (having 10 dB difference or more). In such cases, fading on the relay link has a distribution that is very similar to that of the constituent link with the smallest K-ratio. While, in general, relaying is meant to increase average received SNR, it is shown that when the fast fading on the links constituting a relay channel is i.i.d., N-hop relaying reduces the specular-to-random power ratio on the relay link by N times, or significantly increases the depth of fading. This result indicates a need for significant gains at relay stations in some cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".