Co-Adaptive End-to-End Synergistic AI for Real-Time Traffic Prediction and Load Balancing in Mobile Networks
Bibliographic record
Abstract
Recently, the development of Artificial Intelligence (AI) has improved traffic prediction, Load Balancing (LB) and network automation in mobile systems for enabling more efficient and adaptive management of dynamic resources. However, the existing models of traffic prediction and LB have limited responsiveness under unpredictable network fluctuations and varying service demands. Hence, this research proposes a Co-Adaptive End-to-end Synergistic AI (CAESA) framework for real-time and uncertainty-aware traffic prediction as well as intelligent LB in mobile networks. Here, the proposed CAESA allows the predictor and controller to continuously exchange the feedback and adapt to real-time changes for maintaining stable LB even under sudden traffic flows. Initially, the input data is collected from streaming telemetry and mobility traces and preprocessed with K-Nearest Neighbors (KNN) imputation and min-max normalization to fill missing values and scale features to a uniform range. Similarly, the fixed-length overlapping sliding windows and adjacency-based dynamic graph generation to convert the continuous time series data into sequences and construct evolving graph representations respectively. After that, a Graph Transformer with Temporal Attention (GTTA) is employed to capture spatial dependencies and temporal evolution. Further, the differentiable convex optimization is used to generate the safe LB actions. Finally, the hierarchical co-adaptive control module executes and refines these actions in real-time by using MultiAgent Proximal Policy Optimization (MAPPO) and meta-learning. The proposed CAESA framework achieved better results in terms of throughput (923 Mbps) than the existing FlowBender-Enhanced Reinforcement Learning for LB (FERL-LB) 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".