Cooperative Diversity Routing in Wireless Networks
Bibliographic record
Abstract
In this paper, we explore physical layer cooperative communication in order to design network layer routing algorithms that are energy efficient. We assume each node in the network is equipped with a single omnidirectional antenna and that multiple nodes are able to coordinate their transmissions in order to take advantage of spatial diversity to save energy. Specifically, we consider cooperative diversity at physical layer and multi-hop routing at network layer, and formulate minimum energy routing as a joint optimization of the transmission power at the physical layer and the link selection at the network layer. We then show that as the network becomes larger, finding optimal cooperative routes becomes computationally intractable. As such, we develop a number of heuristic routing algorithms that have polynomial computational complexity, and yet achieve significant energy savings. Simulation results are also presented, which indicate that the proposed algorithms based on optimal power allocation significantly outperform existing algorithms based on equal power allocation, by more than 60% in some simulated scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".