System Capacity Maximization With Efficient Resource Allocation Algorithms in D2D Communication
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Bibliographic record
Abstract
In a device to device (D2D) communication underlaying cellular network, total system sum rate (capacity) can be improved if cellular user equipment's (UEs) and D2D pairs share resource blocks (RBs). We consider an optimization problem where the objective is to maximize the total sum rate of the system by sharing RBs among cellular UEs and D2D pairs while maintaining the quality of service requirements. We consider three approaches depending on the degree of sharing i.e., “One to One Sharing”, “One to Many Sharing”, and “Many to Many Sharing”. Most of the existing algorithms consider that sharing of RBs can only improve the total system sum rate. However, sharing of RBs between a cellular UE and a D2D pair can also decrease the total system sum rate. Considering this observation, we propose an algorithm based on the weighted bipartite matching algorithm which avoids such sharing and maximize the total system sum rate for the “One to One Sharing”approach. Moreover, We propose resource allocation algorithms for “One to Many Sharing”and “Many to Many Sharing”with a target to maximize the system capacity and also provide the analysis of the proposed algorithms. Through simulations, we find that our proposed algorithms outperform the existing algorithms in terms of maximizing total system sum rate. Our proposed algorithms also perform better in terms of total interference introduced due to the sharing of RBs among cellular UEs and D2D pairs.
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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