Efficient Resource Allocation in SCMA-Enabled Device-to-Device Communication for 5G Networks
Why this work is in the frame
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Bibliographic record
Abstract
According to advanced wireless network standards, Device-to-device based communication underlaid conventional cellular network is considered a promising technology to improve the network performance. Precisely, this hybrid architecture provides an efficient resource allocation for cellular and D2D users while increasing the flexible utilization of the spectrum resources. Recently, the sparse code multiple access (SCMA) has been proposed as an efficient non-orthogonal multiple access technology for the 5G network paradigm. The SCMA scheme enhances the spectral efficiency, supports a massive connectivity, and diverses applications by enabling system overloading. Thus, in this paper, SCMA technology is applied to a D2D enabled cellular network, targeted at utilizing the overloading feature of the SCMA scheme to support a massive device connectivity while enhancing the overall network performance. The SCMA scheme is implemented to jointly optimize the codebook and power allocation in the downlink D2D enabled cellular network, with the aim to maximize the system data rate. This joint optimization problem is solved by decomposing the original problem into two sub-problems: codebook allocation and power allocation. For the codebook allocation, the rate aware codebook selection scheme for D2D system (RACBS-D2D) is proposed using conflict graph. For the power allocation solution, a geometric water-filling (GWF) method is utilized to propose the iterative GWF-based power allocation (IGWFPA) scheme. The performance of the proposed schemes is evaluated through simulations that reveal the benefits of the proposed solutions under different scenarios.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it