What is Ray Tracing?

The practical application of ray tracing technology began in the high-definition graphics realm, where what is called a ray tracing unity simulates the interaction of light bouncing off objects in a digitally rendered scene. Telecommunication applications for ray tracing simulation leverage the principal of light/wave duality, taking advantage of the similarities between light and radio wave behavior at high frequencies. This correlation allows light to become a surrogate for 5G or 6G radio waves in network digital twins and other emulation strategies.

Beyond simple line-of-sight calculations, ray tracing takes emulation and digital modeling practices to the next level by precisely recreating the behavior of radio waves as they scatter and reflect off objects or interact with surfaces. This advanced level of modeling allows network planners to predict signal strength and coverage while optimizing cell tower placement and antenna configuration. With so many moving pieces to emulate, ray tracing requires significant computational power to accurately assess RF performance in complex environments.     

Ray Tracing Origin Story

The origin of computer-based ray tracing can be traced back to the 1960s, when the first systems were developed to represent the interaction of light and matter in a three-dimensional space. Since then, research and development efforts have been divided between computer graphics, thermal analysis, and optical design, with all three fields benefitting from the incremental hardware and software advancements that elevated ray tracing technology.   

By the late 1970s, ray tracing pioneers were increasing the volume of rays in their software programs to create more realistic representations, but the limitations of available hardware made rendering times unacceptably long. The idea of partitioning tasks between multiple processors was proposed shortly thereafter, but would take decades to come to fruition. A series of technology breakthroughs, aided by the progression of Moore’s law, have made ray tracing a reliable and accurate simulation tool.

The lofty computing requirements associated with ray tracing can be overcome by adopting one or more hardware accelerated ray tracing techniques. These methods utilize specialized hardware components to speed up ray tracing calculations while providing higher levels of detail and accuracy. Hardware originally developed for 3D modeling and animation applications improves performance and resolution by making use of:

• Ray tracing cores to perform ray intersection calculations and support real time ray tracing emulation.
• Ray accelerators that utilize bounding volume hierarchy (BVH) traversal techniques to accurately predict the outcome of rays interacting with objects.
• High-speed VRAM to support the intensive memory requirements of ray tracing

AI is another important element of hardware accelerated ray tracing, as it helps to reduce the computational load and improve performance by filling in missing details and analyzing partially traced rays. AI and machine learning algorithms allow scenarios to be modeled using a lower ray count while enabling a wider variety of network configurations and beamforming options to be emulated efficiently.

Accelerated Ray Tracing with GPUs

A graphics processing unit (GPU) is an electronic circuit that speeds up a computer’s graphical processing capabilities by performing multiple functions simultaneously or performing the same operation on multiple data values in parallel. GPUs are also one of the keys to the accelerated ray tracing used to support 5G and 6G network testing and emulation.

The thousands of individual cores in a ray tracing GPU make the large number of calculations needed for ray tracing possible. Some of most advanced products also incorporate AI capabilities to reduce noise and improve quality. The premier GPU options for ray tracing include:  

• Nvidia RTX: High end GPUs in the RTX series are designed to support complex product, design, and architectural models. Nvidia has made significant breakthroughs in real time ray tracing to elevate the technology beyond its original gaming and animation applications.
• AMD Radeon RX: As a major player in the GPU market, AMD has developed their own innovative architecture to support ray tracing and AI accelerators. Advanced “super resolution” capabilities and minimized latency make the Radeon RX series an ideal tool for diverse emulation applications.

The top GPU manufacturers continue to release new and improved products each year, with upscaled frame generation performance supporting the growing demand for ray tracing in new and traditional applications. The extension of advanced GPU technology into wireless network emulation has been a breakthrough for 5G and 6G modeling.

How is Ray Tracing Supporting Real Environment Modeling in 5G and 6G Technology?  

Ray tracing applications for 5G and 6G networks are nearly unlimited, as they enhance the power of digital test beds to accurately model a wide variety of micro scenarios. Ray tracing simulation processes can recreate beams bouncing within buildings and dense urban environments, or multiple access technologies interacting in overloaded networks.

Beyond the valuable test applications, ray tracing also provides the real environment modeling prowess needed to enhance 5G and 6G technology development through:     

• Deterministic channel modeling: While stochastic modeling techniques rely on random variables to simulate channel characteristics and behavior, ray tracing technology is capable of generating deterministic channel simulations, where characteristics like signal strength, delay, and phase can be calculated precisely based on the antenna placement and geometric obstacles found in the environment.
• City-scale digital twins: Digital twins of objects and systems, including complete wireless networks, allow the conditions experienced in the real world to be modeled and assessed rapidly through their digital counterparts. 6G ray tracing technology can provide radio frequency (RF) propagation modeling for digital twins of entire cities, as researchers and engineers prepare for rollout. KPIs applied in the digital twin effectively measure the impact of network conditions on application performance.  
• Power optimization: Intelligent base station utilization and antenna orientation are prerequisites for expanding the commitment to sustainable practices from 5G to 6G and beyond. Ray tracing makes it possible to predict signal coverage and throughput over the course of the day, and apply this insight to autonomous network optimization practices.

As the applications for ray tracing in large-scale digital twins and 6G research and development continue to expand, additional use cases for test and simulation are also evolving. The ability to accurately model the behavior of radio waves provides network testing benefits and efficiencies before, during, and after deployment, with interference, signal strength, and coverage accurately predicted. The ray tracing benchmark also provides a useful basis of comparison between the lab and real-world network performance. Additional test and simulation applications include:    

• Beamforming optimization: The beamforming techniques that distinguish 5G and 6G from previous wireless generations require the most expedient signal paths to reach user devices to be continually re-calculated. This includes indirect or reflected signal paths chosen to compensate for millimeter wave limitations. Ray tracing provides an ideal test tool to analyze new and existing beamforming methods and optimize antenna configurations. 
• Coverage gap identification: Ray tracing establishes a means to simulate signal propagation from multiple base stations, then generate detailed coverage maps for an unlimited number of configurations. This allows coverage gaps or areas with inadequate signal strength to be identified and corrected more effectively.
• Environmental modeling: 3D modeling techniques to recreate buildings, trees, and other obstacles have continually advanced. A ray tracing simulator recreates signal propagation within this digital test environment. While RAN Scenario Generation (RSG) provides a broad view of system performance, ray tracing excels at fine tuning small clusters of nodes.   

The VIAVI OneAdvisor 800 platform supports 6G real time ray tracing research focused on autonomous AI network optimization and energy saving use cases. Working in conjunction with the VAVI XEdge sensor, OneAdvisor collects site-specific data for continuous real time auto-calibration. Demonstrations conducted by VIAVI Marconi Labs® show the exciting potential of ray tracing for advanced network coverage and throughput optimization.

The VIAVI TeraVM AI RSG forms a perfect counterpart for advanced ray tracing simulation by establishing a RAN Digital Twin to mirrors real network conditions. This versatile tool can emulate a wide variety of topologies, configurations, and mobility patterns and aid in the training and testing of AI apps found in RAN Intelligent Controllers (RIC).