Efficient resource allocation for device-to-device communication underlaying LTE network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Device-to-device (D2D) communication as an underlaying cellular network empowers user-driven rich multimedia applications and also has proven to be network efficient offloading eNodeB traffic. However, D2D transmitters may cause significant amount of interference to the primary cellular network when radio resources are shared between them. During the downlink (DL) phase, primary cell UE (user equipment) may suffer from interference by the D2D transmitter. On the other hand, the immobile eNodeB is the victim of interference by the D2D transmitter during the uplink (UL) phase when radio resources are allocated randomly. Such interference can be avoided otherwise diminish if radio resource allocated intelligently with the coordination from the eNodeB. In this paper, we formulate the problem of radio resource allocation to the D2D communications as a mixed integer nonlinear programming (MINLP). Such an optimization problem is notoriously hard to solve within fast scheduling period of the Long Term Evolution (LTE) network. We therefore propose an alternative greedy heuristic algorithm that can lessen interference to the primary cellular network utilizing channel gain information. We also perform extensive simulation to prove the efficacy of the proposed algorithm.
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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 | 0.000 | 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 it