WiThRay: A Versatile Ray-Tracing Simulator for Smart Wireless Environments
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Abstract (arXiv)
This paper presents the development and evaluation of WiThRay, a new wireless three-dimensional ray-tracing (RT) simulator. RT-based simulators are widely used for generating realistic channel data by combining RT methodology to get signal trajectories and electromagnetic (EM) equations, resulting in generalized channel impulse responses (CIRs). This paper first provides a comprehensive comparison on methodologies of existing RT-based simulators. We then introduce WiThRay, which can evaluate the performance of various wireless communication techniques such as channel estimation/tracking, beamforming, and localization in realistic EM wave propagation. WiThRay implements its own RT methodology, the bypassing on edge (BE) algorithm, that follows the Fermat's principle and has low computational complexity. The scattering ray calibration in WiThRay also provides a precise solution in the analysis of EM propagation. Different from most of the previous RT-based simulators, WiThRay incorporates reconfigurable intelligent surfaces (RIS), which will be a key component of future wireless communications. We thoroughly show that the channel data from WiThRay match sufficiently well with the fundamental theory of wireless channels. The virtue of WiThRay lies in its feature of not making any assumption about the channel, like being slow/fast fading or frequency selective. A realistic wireless environment, which can be conveniently simulated via WiThRay, naturally defines the physical properties of the wireless channels. WiThRay is open to the public, and anyone can exploit this versatile simulator to develop and test their communications and signal processing techniques.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2023.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 1.
6 papers.
- 2023Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approachescited by 7
- 2024Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Controlcited by 3
- 2025Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Questioncited by 3
- 2024Vision-Assisted Digital Twin Creation for mmWave Beam Managementcited by 1
- 2024SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogatecited by 1
- 2023Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennascited by 0
- 2025Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question15 Sep 2025
- 2024SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate13 Nov 2024
- 2024Vision-Assisted Digital Twin Creation for mmWave Beam Management31 Jan 2024
- 2024Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control23 Jan 2024
- 2023Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas30 Oct 2023
- 2023Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches14 Oct 2023