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In-Person Poster presentation / poster accept

WiNeRT: Towards Neural Ray Tracing for Wireless Channel Modelling and Differentiable Simulations

Tribhuvanesh Orekondy · Kumar Pratik Kumar Pratik · Shreya Kadambi · Hao Ye · Joseph Soriaga · Arash Behboodi

MH1-2-3-4 #37

Keywords: [ wireless ] [ nerf ] [ ray tracing ] [ neural rendering ] [ Applications ]


Abstract: In this paper, we work towards a neural surrogate to model wireless electro-magnetic propagation effects in indoor environments.Such neural surrogates provide a fast, differentiable, and continuous representation of the environment and enables end-to-end optimization for downstream tasks (e.g., network planning). Specifically, the goal of the paper is to render the wireless signal (e.g., time-of-flights, power of each path) in an environment as a function of the sensor's spatial configuration (e.g., placement of transmit and receive antennas). NeRF-based approaches have shown promising results in the visual setting (RGB image signal, with a camera sensor), where the key idea is to algorithmically evaluate the 'global' signal (e.g., using volumetric rendering) by breaking it down in a sequence of 'local' evaluations (e.g., using co-ordinate neural networks). In a similar spirit, we model the time-angle channel impulse response (the global wireless signal) as a superposition of multiple paths. The wireless characteristics (e.g., power) of each path is a result of multiple evaluations of a neural network that learns implicit ray-surface interaction properties. We evaluate our approach in multiple indoor scenarios and demonstrate that our model achieves strong performance (e.g., $<$0.33ns error in time-of-flight predictions). Furthermore, we demonstrate that our neural surrogate whitens the `black-box' wireless simulators, and thus enables inverse rendering applications (e.g., user localization).

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