Joint Admission and Power Control for Massive Connections via Graph Neural Network
Bibliographic record
Abstract
The sixth-generation mobile communication system (6G) puts forward higher requirement for connection density, which is difficult to meet with the existing resource management schemes in real time. In this paper, we investigate the graph neural network (GNN) based algorithms for supporting the massive connectivity in 6G. Using the power intensity of the received signal or signal-to-interference-plus-noise ratio (SINR) as a measure of communication quality, we aim to maximize the number of links that meet quality of service (QoS) requirements in a given area through joint admission and power control. Specifically, we consider two models. Among them, the blocking interference model presets the transmit power of the link in advance, and only needs admission control. After the original problem is converted to the maximum independent set (MIS) problem, we design a solution based on graph convolution network (GCN) and Q-learning. The accumulative interference model considers all the interference in the scene and controls the power and access jointly. For this model, we propose an algorithm based on graph attention network (GAT). Simulations demonstrate that the proposed GNN based algorithms preserve small computation time and achieve significant performance gain even with large network scale. As such, they are very suitable for the 6G scenario with massive connections.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".