Instinct JCAS

A configurable simulator for Joint Communication and Sensing (JCAS) networks on a toroidal domain — stochastic-geometry deployments, mobility, physical channel models, and Kalman-filter object tracking.

Link to the Github repo. The simulator runs from the notebook main.ipynb or as a Streamlit web app (simulator_interface.py). Developed within the EU-funded INSTINCT project.

This project provides a configurable simulator for Joint Communication and Sensing (JCAS) networks defined on a periodic (toroidal) spatial domain. Base stations, mobile user equipments and sensing objects are placed on a rectangular flat torus, where a Voronoi tessellation defines cell coverage; the periodic geometry removes boundary effects and yields unbiased spatial statistics. Deployments are drawn from Poisson or Binomial point processes, with users and targets placed either uniformly or as Gaussian clusters.

Communication is modelled through Lindley queues with SINR-dependent service rates, over either a ray-traced channel parameterised from University of Oulu measurements or a Rayleigh-fading power-law model. Sensing objects move under stationary, Gauss–Markov, or constant-speed mobility, and are tracked with Kalman / Extended Kalman filters for linear and nonlinear observations. Optional beamforming and TDD scheduling are supported.

The simulator reports coupling metrics — association ratios and Pearson correlations — that quantify the trade-off between the communication and sensing functions sharing the same infrastructure.

Features

  • Network generation via Poisson or Binomial point processes; UEs and sensing objects placed uniformly or as Gaussian clusters.
  • Toroidal geometry with Voronoi cell coverage, eliminating edge effects.
  • Mobility models: stationary, Gauss–Markov, and constant-speed motion.
  • Two channel models: ray-traced (Oulu measurements) and Rayleigh-fading power-law.
  • Communication over Lindley queues with SINR-dependent service rates.
  • Sensing and tracking with Kalman / Extended Kalman filters.
  • Optional beamforming and TDD scheduling.
  • Communication–sensing coupling metrics (association ratios, Pearson correlations).

Usage

Requires Python 3.10+.

git clone https://github.com/emanuelemengoli/instinct-jcas-simulator.git
cd instinct-jcas-simulator
./setup.sh
source .venv/bin/activate

Then either launch the web app with streamlit run simulator_interface.py, or open main.ipynb.

Released under the MIT License.