Improving the Energy Efficiency by Cooperative Transmission in Multi-Hop Wireless Ad-Hoc Networks
Bibliographic record
Abstract
Cooperative transmission has been recently proposed as a promising technique to combat multi-path fading and increased link reliability. It represents a potential candidate to exploit the benefits of using multiple antennas system without requiring to implement multiple antennas per terminal. There has been extensive research investigating physical layer issues of such systems; however, higher layer protocols that exploit cooperative links in ad hoc networks are still emerging in cooperative ad hoc networks, and it is important to effectively use cooperation without affecting the performance of the network. In this dissertation, we proposed a novel a characterization of the optimal multi-hop cooperative routing in ad hoc networks, and developed a metric for both evaluation. The key advantages of cooperative links are to minimize the number of hops while maintaining the QoS requirements and to minimize the end-to-end total power for a given rate. Also we showed that energy can be used more efficiently if we determine the joint optimal packet size and the optimal power allocation for both the source and the relay. For multi-flow scenario, we have proposed a clique-based inter-flow interference abstraction, and used the linear programming formulation to study the capacity gain of ad-hoc cooperative network. It is observed that the network capacity in multi-hop multi-flow settings is severely affected by interference between links and this effect increases when the cooperative relaying is imposed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".