Adaptive Resource Allocation for 6G Network Slicing via Hybrid CNN-LSTM Architecture
Bibliographic record
Abstract
Network slicing enables multiple virtual networks on shared 6G infrastructure, but dynamic resource allocation across Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC) services remains challenging. We present a hybrid Convolutional Neural Network-Long Short-Term Memory Architecture (CNN-LSTM) framework with service-specific utility functions that optimize resources while ensuring Quality of Service (QoS) guarantees under dynamic conditions. Our approach integrates spatial pattern recognition with temporal prediction, incorporating constraint measurement and lightweight optimization. Experimental results on a testbed with 100 base stations and 10,000 users demonstrate superior performance over state-of-the-art methods. The framework achieves significant improvements in resource utilization, QoS satisfaction, and energy efficiency with real-time inference capability. Convergence analysis validates system stability, confirming practical deployment feasibility for latency-critical 6G applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".