Resource Allocation for D2D Communication Underlaid Cellular Networks Using Graph-Based Approach
Bibliographic record
Abstract
In this paper, we study the non-orthogonal dynamic spectrum sharing for device-to-device (D2D) communications in the D2D underlaid cellular network. Our design aims to maximize the weighted system sum-rate under the constraints that: 1) each cellular or active D2D link is assigned one subband and 2) the required minimum rates for cellular and active D2D links are guaranteed. To solve this problem, we first characterize the optimal power allocation solution for a given subband assignment. Based on this result, we formulate the subband assignment problem by using the graph-based approach, in which each link corresponds to a vertex and each subband assignment is represented by a hyper-edge. We then propose an iterative rounding algorithm and an optimal branch-and-bound (BnB) algorithm to solve the resulting graph-based problem. We prove that the iterative rounding algorithm achieves at least 1/2 of the optimal weighted sum-rate. Extensive numerical studies illustrate that the proposed iterative rounding algorithm significantly outperforms the conventional spectrum sharing algorithms and attains almost the same system sum-rate as the optimal BnB algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".