Efficient Interference-Aware D2D Pairing for Collaborative Data Dissemination
Bibliographic record
Abstract
To offer a large capacity in the fifth-generation (5G) mobile networks, a promising technique is to integrate and utilize the intelligence and resources of smart devices at the mobile edge. In this paper, we investigate how to exploit device collaboration to facilitate data dissemination via device-to-device (D2D) communications. In particular, we focus on a key research problem that aims to effectively pair request devices with cache devices in close proximity. Due to the interference among D2D links, it is computationally hard to obtain an optimal pairing for a large-scale network. Hence, we propose an interference-aware approach that can obtain a near- optimal approximation result efficiently. Specifically, the proposed approach first uses Lagrangian relaxation to find an upper-bound solution, and then derives a feasible solution from the initial pairing and further augments it. Extensive simulation results show that our approach performs closely to the optimal solution and achieves significant performance gain over the existing schemes in terms of the ratio of matched device pairs and total sum rate.
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 | 0.001 | 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".