Device-to-Device Data Transfer Through Multihop Relay Links Underlaying Cellular Networks
Bibliographic record
Abstract
With the increasing demand for high-data mobile traffic and applications, Device-to-Device (D2D) communication has emerged as a promising technique for next generation of cellular networks to increase the network capacity. In this paper, to offload traffic from network backhaul, we investigate the problem of relaying data through multihop nodes, using D2D communication, for a set of transmitter-receiver pairs, by sharing cellular resource blocks. Furthermore, the problem is extended to schedule the transmission links within a maximum time frame to obey a delay constraint. If the transmission path could not be scheduled within the maximum time frame for any D2D communication pair, the communication is redirected through the base station similar to cellular mode. Our problem aims at minimizing the overall D2D transmission power by giving priority to maximizing the number of paths that go through D2D relaying. We mathematically formulate the above problem and find optimal solutions for small networks. We show the NP-hardness of the problem, and owing to its complexity, we propose an algorithmic method for more extensive networks. Through numerical results, the performance of our mathematical optimization model and algorithm is compared and the efficiency of the proposed algorithmic method is shown.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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; 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".