emrass-world-model 0.1.0

A lightweight simulation library for wireless sensing and electromagnetic world modeling.

emrass-world-model 📡

em-world-model is a lightweight electromagnetic world modeling and simulation library designed for Embodied AI and 6G communications. It aims to bridge the gap between traditional visual perception and physical-layer EM simulation, providing AI agents with an "Electromagnetic Perspective" for environment understanding.

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🌟 Key Features

• Physics-Based Simulation Engine: Built-in models for Free Space Path Loss (FSPL), Two-Ray Ground Reflection, and complex Multipath Fading.
• Environmental Semantic Modeling: Supports EM scattering and reflection profiling for common materials such as concrete, glass, and metal.
• ISAC Integration: Supports preliminary simulations for Integrated Sensing and Communication, enabling joint waveform and perception analysis.
• Lightweight & Scalable: Minimal dependencies (NumPy based), making it easy to integrate into Reinforcement Learning (RL) environments or autonomous driving simulators like CARLA or AirSim.

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🚀 Quick Start

Installation

Install the stable version from PyPI:

pip install emrass-world-model

Basic Usage: Calculating Path Loss
from em_model.physics import free_space_path_loss

# Parameters: distance (meters), frequency (Hz)
distance = 150 
frequency = 28e9  # 28 GHz mmWave

loss = free_space_path_loss(distance, frequency)
print(f"Path loss at {distance}m: {loss:.2f} dB")