Cooperative techniques for energy-efficient wireless communications
Bibliographic record
Abstract
Introduction Cooperative communication techniques are envisioned as an integral part of nextgeneration wireless networks. Cooperative communication is based on extending the interactions between different communications nodes to obtain ubiquitous network access with the required quality of service (QoS). The virtual antenna array created by collaborating distributed communication nodes provides the network with a spatial diversity merit without the need to equip the nodes with multi-antenna transceivers. In addition to combating fading through spatial diversity, cooperative communication is a powerful technique to increase spectral efficiency, reduce energy consumption, and extend the network coverage with a lower cost than traditional networks [1, 2]. All the aforementioned advantages of cooperative communication encouraged the inclusion of relaying techniques in the International Mobile Telecommunication (IMT)-advanced fourth-generation (4G) standards IEEE 802.16m and Long Term Evolution-Advanced (LTE-A). These standards consider the two-hop relaying technique in their design, which is one of the well-studied cooperative transmission schemes. Multi-hop relaying is included in IEEE 802.16j, and other cooperative schemes, such as relay selection cooperative communication, are expected to be included in future generations of wireless communication networks to improve connectivity, achieve higher data rates, and reduce energy consumption compared to current networks [1, 3]. Energy-aware design is one of the main targets for the next generation of communication networks. This design helps both energy-constrained wireless devices and base stations to save energy and effectively work toward green communication solutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".