Cooperative wireless multicast: performance analysis and power/location optimization
Bibliographic record
Abstract
The popularity of multimedia multicast/broadcast applications over wireless networks makes it critical to address the error-prone, heterogeneous and dynamically changing nature of wireless channels. A promising solution to combat channel fading is to explore the cooperative diversity in which users may help each other forward packets. This paper investigates cooperative multicast schemes that use a maximal ratio combiner to enhance the received signal-to-noise ratio (SNR), and provides a thorough performance analysis. Two relay selection schemes are considered: the distributed and the genie-aided cooperation schemes. We derive the closed-form formulation and the approximations of their average outage probabilities.We also analyze the optimal power allocation and relay location strategies, and show that allocating half of the total transmission power to the source minimizes the average outage probability. Our analysis and simulation results show that cooperative multicast gives better performance when more relays help forward signals. Cooperative multicast helps achieve diversity order 2, and user cooperation can significantly reduce the outage probability, especially in the high SNR region. Finally, we compare the two cooperation strategies, and show that distributed cooperative multicast is preferred since it achieves a lower outage probability without introducing extra overhead for control messages.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".