Cooperative Device-to-Device Communication for Uplink Transmission in Cellular System
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Bibliographic record
Abstract
The rapid development of the Internet of Things has brought new challenges to cellular networks with super-dense devices and deep-fading channels. These challenges may substantially decrease the transmission efficiency and increase the device's power consumption, especially in the uplink. A pressing issue is to improve enhanced Node B's (eNB) scheduler considering a large number of users. In this paper, a semi-centralized cooperative control method is proposed for the cellular uplink transmissions, where the user equipment (UE) relays are randomly selected according to a certain density decided by the eNB. Two specific cooperative schemes based on device-to-device (D2D) communications are proposed, which are the random UE relay scheme and the one further applying network coding. The D2D interference is considered and modeled based on stochastic geometry. The proposed schemes are analyzed based on two distinct traffic models, i.e., the machine type communications traffic with the small-data feature and the full-buffer traffic. Extensive Monte Carlo simulations have been conducted for the small-data traffic and the closed-form theoretical results have been derived for the full-buffer traffic. Performance gains are achieved in various scenarios and the comparisons between two cooperative schemes are made as well. The results provide an important guideline for the eNB to determine how to select and configure cooperative D2D communication for uplink.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.000 |
| 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 it