Bandwidth Prediction in 5G Mobile Networks Using Informer
Bibliographic record
Abstract
Fifth-generation (5G) mobile networks aspire to deliver exceptionally high data rates with ultra-reliable and low-latency connectivity. With the growing popularity of mobile Internet and the increased bandwidth requirements of mobile applications, user Quality of Experience (QoE) is becoming increasingly critical. 5G networks demand predicting the real-time bandwidth of a channel to satisfy the QoE for bandwidth-savvy applications such as video streaming/conferencing, vir-tual/augmented/mixed reality, and autonomous driving. If future bandwidth can be forecast in advance, the bandwidthhungry applications may utilize the estimates to adapt their data transmission rates and dramatically enhance user QoE. By analyzing a publicly available 5G dataset comprised of the channel, context, and cell-related metrics with throughput information, existing work has used Long Short Term Memory (LSTM) based mechanisms to predict future bandwidth. We applied the Transformer-based model, namely ‘Informer,’ to the 5G dataset and found significant improvement of about 95% error decrease for bandwidth prediction. In addition, we combined some new feature analysis approaches (LASSO and Random Forest with new hyper-parameters) in addition to the the existing Random Forest with Informer to find out the most accurate prediction approach.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".