Wireless Resource Virtualization With 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
Wireless resource virtualization is a potential solution for meeting the increasing demand for mobile data services. Virtualization allows for more efficient utilization of the spectrum, reduces capital expenditures and operating expenditures, and can support higher peak rates. Device-to-device (D2D) communication as an underlay to cellular networks is also a potential solution to satisfy the data demand. Due to the proximity of devices and thus the higher signal-to-interference and noise ratio, higher data rates can be achieved using D2D communication. This is beneficial in cases of multimedia sharing where data can be broadcast to several nearby users. However, the interference that D2D pairs introduce to cellular users should be below a target threshold so as not to reduce their performance. In this paper, the problem of wireless resource virtualization with D2D communication underlaying the LTE network is formulated. Since the problem is an integer non-linear programming problem, it is divided into two smaller linear integer programs that are solved to optimality. Two lower complexity heuristic algorithms, each solving one of the subproblems are introduced. Results show that the heuristic achieves close to optimal results while having a much lower computational complexity.
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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 | 0.001 |
| 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