Finite‐state Markov modelling for wireless cooperative networks
Bibliographic record
Abstract
Finite‐state Markov chain (FSMC) models can capture the essence of time‐varying fading channels and they are important tools for wireless network protocol design and performance study. How to build FSMC models for multi‐hop and multi‐path wireless systems remains an open issue. In this study, the FSMC models are developed for amplify‐and‐forward (AF) cooperative systems with selection combining (SC) and maximum ratio combining (MRC) techniques, respectively. First, the second‐order statistics, the level‐crossing rate and the average fade duration, are derived based on the statistical properties of each individual path, for the AF cooperative systems with SC and MRC, respectively. The results reveal that, in addition to improving the average received signal‐to‐noise ratio, the diversity combining schemes also improve its second‐order statistical properties. Simulation results are given to verify the accuracy of the developed FSMC models. Finally, the models are used to optimise the configuration for scalable video streaming in an AF cooperative network. Experimental results show the feasibility and advantage of applying the proposed FSMC models for assisting protocol design and optimisation 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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".