On Using D2D Collaboration and a DF-CF Relaying Scheme to Mitigate Channel Interference
Bibliographic record
Abstract
Given the exponentially increasing number of connected devices to the network which will lead to a larger number of installed celluar towers and base stations that are in closer proximity to one another when compared to the current cellular network setup, and the increasing demand of higher data rates by end users, it becomes essential to investigate new methods that will more effectively mitigate the larger interference introduced by the more packed celluar grid and that result in higher data rates. This paper investigates using Device-to-Device communication where neighboring users can cooperate to mitigate the correlated interference they both receive, where one user acts as a relay and the other as the intended destination of a broadcast message sent by the source base station. The setup studied utalizes a non-orthogonal multiple access (NOMA) scheme and a combined decode-forward and compress-forward relaying scheme. We show that this combined scheme outperforms the individual schemes for some channels and network setups, or reduces to either scheme when the combination does not offer any achievable rate gains. The performance of each scheme is measured with respect to the locations of the base station and the two devices, and to the capacity of the digital link between the users.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".