Second-Order Properties for Wireless Cooperative Systems with Rayleigh Fading
Bibliographic record
Abstract
Second-order statistical parameters of wireless channels, such as level crossing rate (LCR) and average fade duration (AFD), determine how frequent and the burst length of the channel in bad conditions, so they play an important role in the performance of wireless communication systems. For user-cooperative wireless systems, due to the interactions of multiple channels, it is non-trivial to determine the LCR and AFD of the received signals, which is an open issue. In this paper, we develop an analytical framework to quantify the LCR and AFD of the amplify-and-forward (AF) cooperative system using selection combining (SC) over Rayleigh fading channels. We first analyze the statistics of the two independent fading paths, the AF relay path with a mobile-to-mobile (M2M) channel and a mobile-to-fixed (M2F) Rayleigh fading channel, and the direct path with a M2F Rayleigh fading channel. Then, we derive the expressions of the second-order statistical parameters of the AF cooperative system with SC. Numerical results verify the correctness of our model. The analytical and simulation results reveal the different effect of the motions of the source and the relay nodes, and they can be used to select better relay nodes and assist the design and optimization of error control mechanisms in different layers in wireless cooperative networks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".