Bayesian-Optimized TCNN-LSTM: Cellular Data for Adaptive Handover Prediction in 5G O-RAN

5.0 MB of cellular network data capturing key parameters such as network quality and available bandwidth. The dataset is designed for training adaptive handover algorithms in O-RAN environments, enabling efficient and seamless connectivity. Authored by Paul Jose and last updated on April 3, 2026, it is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Predicting optimal handover timing based on network quality parameters mentioned in the description
• Optimizing bandwidth allocation algorithms based on available bandwidth data
• Training deep learning models like TCNN-LSTM for adaptive connectivity management in O-RAN
• Benchmarking Bayesian optimization techniques for hyperparameter tuning in network prediction tasks

Strengths

• File size is precisely 5.0 MB, indicating a manageable download.
• License is clearly specified as CC-BY-4.0, allowing for open reuse with attribution.
• Platform tags provide clear domain context, including 'Cellular Data', '5g Networks', and 'Time Series Analysis'.

Limitations

• Row count is unknown, which may limit suitability assessment for large-scale training.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Paul Jose via figshare

Collection Method

Likely collected from cellular network measurements or simulations, as suggested by the description of capturing key parameters.

Time Range

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Freshness

Last updated 2026-04-03 17:48:15; freshness should be verified.

Geography

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