Performance Analysis of Semi-Centralized Controlled Uplink Cooperative Transmission
Bibliographic record
Abstract
The rapid development of the Internet of Things (IoT) has brought big challenges to the traditional cellular networks such as super dense devices and deep fading channels. These new challenges will lead to a significant transmission efficiency degradation and increase the device's power consumption, especially in the uplink. A huge pressure will be also imposed to the enhanced Node B's (eNB) scheduler due to the large number of users. In this paper, a semi- centralized controlled cooperative method is proposed for the uplink cellular transmission, where the User Equipment (UE) relay will be randomly selected according to a certain density decided by the eNB. Two specific cooperative schemes based on the Device-to- Device (D2D) are proposed, which are the random UE relay scheme and the one further combined with the Network Coding (NC). The theoretical analyses for both of them are given and corresponding closed-form results are derived. The D2D interference is considered and modelled based on the stochastic geometry. The performance gains are identified by numerical evaluations in various scenarios and the comparisons between two cooperative schemes are made as well. Also, these results can provide an important guideline for the eNB to determine the optimal density of the UE relays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".