Generating more Realistic Packet Loss Patterns for Wireless links using Neural Networks - Daniel Otten; Thomas Hänel; Tim Römer; Nils Aschenbruck (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
Simulations of wireless network connections are essential for the development of new technologies because they are far more scalable than real-world experiments and reproducible. Modeling packet loss realistically provides a highly abstract yet powerful tool for the simulation of wirelesses links. Typi- cally, simple statistical models or replaying of recorded traces are used for the simulation. For a proper parametrization of simple statistical models, recorded traces are required, too. Both approaches have drawbacks: replaying traces is limited to the length of the traces, a repetition may lead to unwanted effects in the simulation. The statistical models solve this, but the resulting packet loss patterns significantly differ from real ones. In this paper, we propose using a neural network in- stead. It takes the same kind of input, i.e., a real-world trace, but it can generate longer traces with more realistic loss pat- terns. We share pre-trained neural networks for multiple links in office and industry scenarios with the community for use in future research.