Cross-layer scheduling design for cooperative wireless two-way relay networks
Bibliographic record
Abstract
Introduction Background. The degrees of freedom introduced by multiple antennas at the transmitters and receivers of wireless communication systems facilitate multiplexing gains and diversity gains. A wireless point-to-point link with M transmit and N receive antennas constitutes an M -by- N multiple-input multiple-output (MIMO) communication system. The ergodic capacity of an M -by- N MIMO fading channel increases almost linearly with min{ M,N } provided that the fading meets certain mild conditions. Hence, it is not surprising that MIMO has attracted a lot of research interest since it enables significant performance and throughput gains without requiring extra transmit power and bandwidth. However, limitations on the number of antennas that a wireless device is able to support as well as the significant signal processing power and complexity required in MIMO tranceivers limit the gains that can be achieved in practice. To overcome the limitations of traditional MIMO, the concept of cooperative communication has been proposed for wireless networks such as fixed infrastructure cellular networks and wireless ad-hoc networks. The basic idea of cooperative communication is that the single-antenna terminals of a multiuser system can share their antennas and create a virtual MIMO communication system. Thereby, three different types of cooperation may be distinguished, namely, user cooperation, base station (BS) cooperation, and relaying. Theoretically, user cooperation and BS cooperation are able to provide huge performance gains, when compared with noncooperative networks. However, the required information exchange between users and BSs may make these options less attractive in practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".