Two-Stage GNN-Based Scalable Access Mode Selection and Power Control for Cell-Free and D2D Heterogeneous Networks
Bibliographic record
Abstract
Cell-free and device-to-device (D2D) heterogeneous networks provide a promising architecture for seamless and high-capacity coverage through dense deployment while reducing fronthaul load. However, spectrum reuse among heterogeneous transmission links would cause severe interference that restricts network capacity and coverage improvement. The dense deployment further challenges efficient interference coordination due to increased computational cost. To this end, this paper proposes a two-stage graph neural network (GNN) structure for access mode selection and power control in cell-free and D2D heterogeneous networks to achieve effective interference coordination with high computational efficiency. First, we derive closed-form expressions for achievable rates of both cell-free and D2D links under limited fronthaul capacity. Then, we represent the network as a heterogeneous graph and design a two-stage GNN based algorithm. The first stage of our proposed algorithm utilizes edge attention mechanism to optimize access mode selection, and its second stage exploits edge message passing to determine power control. To ensure solution feasibility and algorithm convergence, we introduce modified output layers, binary variable regression, and a penalty-based loss function. Simulation results show that our proposed algorithm can improve network capacity and converge to a near-optimal solution across different network scales, compositions, and key parameters, exhibiting well scalability and generalization.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".