# Zen: LSTM-based generation of individual spatiotemporal cellular traffic with interactions

arXiv: https://arxiv.org/abs/2301.02059 · HTML: https://mlsanity.com/paper/2301.02059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 4 of 4)

- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Continuous and Discrete-Time Survival Prediction with Neural Networks](https://mlsanity.com/paper/1910.06724/) (2019)
- [Energy Optimization in Ultra-Dense Radio Access Networks via Traffic-Aware Cell Switching](https://mlsanity.com/paper/2007.04133/) (2020)
- [Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata](https://mlsanity.com/paper/1810.12909/) (2018)

## Built on by (most cited first, 1 of 1)

- [When Plausible Is Not Realistic: Evaluating Human Mobility in LLM-Based Urban Simulation](https://mlsanity.com/paper/2606.13835/) (2026)

## Abstract (arXiv)

Domain-wide recognized by their high value in human presence and activity studies, cellular network datasets (i.e., Charging Data Records, named CdRs), however, present accessibility, usability, and privacy issues, restricting their exploitation and research reproducibility.This paper tackles such challenges by modeling Cdrs that fulfill real-world data attributes. Our designed framework, named Zen follows a four-fold methodology related to (i) the LTSM-based modeling of users' traffic behavior, (ii) the realistic and flexible emulation of spatiotemporal mobility behavior, (iii) the structure of lifelike cellular network infrastructure and social interactions, and (iv) the combination of the three previous modules into realistic Cdrs traces with an individual basis, realistically. Results show that Zen's first and third models accurately capture individual and global distributions of a fully anonymized real-world Cdrs dataset, while the second model is consistent with the literature's revealed features in human mobility. Finally, we validate Zen Cdrs ability of reproducing daily cellular behaviors of the urban population and its usefulness in practical networking applications such as dynamic population tracing, Radio Access Network's power savings, and anomaly detection as compared to real-world CdRs.