Deep Learning-Based Throughput Prediction in 5G Cellular Networks
Bibliographic record
Abstract
Ahstract-5G technology has ushered in a new era of cellular networks characterized by unprecedented speeds and connectivity. However, these networks' dynamic and complex nature presents significant challenges in network management and Quality of Service (QoS) assurance. In this context, accurate throughput prediction is essential for optimizing net-work resources, improving traffic management, and enhancing user experiences. This study presents novel deep learning approaches utilizing Long Short-Term Memory (LSTM), Bi-directional LSTM (BiLSTM), and Artificial Neural Networks (ANN) to predict the throughput. The methodology achieves exceptional performance, surpassing existing methods. The motive behind leveraging deep learning algorithms is their exceptional ability to capture temporal dependencies and patterns within time-series data, which is intrinsic to network traffic. By employing these models, we can forecast network throughput with high precision, facilitating proactive resource allocation and congestion avoidance. Our approach maintains high QoS and supports cost efficiency and adaptive network maintenance. The BiLSTM and LSTM model's adaptability and learning capabilities make it well-suited for the ever-evolving 5G landscape, where user demands and network conditions fluctuate rapidly. This study demonstrates the technical feasibility and benefits of using BiLSTM and LSTM for overall throughput prediction. It highlights the broader implications for the future of 5G network management and optimization.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".