{"id":173,"date":"2025-03-12T05:47:50","date_gmt":"2025-03-12T09:47:50","guid":{"rendered":"https:\/\/blogs.mathworks.com\/semiconductors\/?p=173"},"modified":"2025-07-21T12:20:57","modified_gmt":"2025-07-21T16:20:57","slug":"connecting-matlab-with-nvidia-aerial-omniverse-digital-twin-for-6g-research","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/semiconductors\/2025\/03\/12\/connecting-matlab-with-nvidia-aerial-omniverse-digital-twin-for-6g-research\/","title":{"rendered":"Connecting MATLAB with NVIDIA Aerial Omniverse Digital Twin for 6G Research"},"content":{"rendered":"

6G will unlock new use cases for wireless cellular networks, such as sensing and building digital twins. To support these innovations, integrating advanced link-level simulations with ray tracing delivers high simulation accuracy in performance evaluations. This combination enables precise modelling of high-frequency wave propagation in complex environments, ensuring robust and efficient network designs. By providing detailed insights into real-world scenarios, a combined MATLAB\/NVIDIA solution accelerates innovation and supports the ambitious targets of 6G.<\/p>\n

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Examples from 6G Exploration Library for 5G Toolbox<\/p><\/div><\/p>\n

Have a question or need more details? Reach out to us at digitaltwin@mathworks.com<\/a><\/pre>\n
For researchers engaged in 6G studies, the 6G Exploration Library<\/a> from MathWorks provides resources to advance beyond the existing 5G standard. This tool allows engineers to conduct detailed link-level simulations, covering aspects such as RF impairments, beamforming, and beyond-5G subcarrier spacings and bandwidths. The code is open and modular, facilitating easy replacement of components such as the channel model for extended exploration.<\/p>\n
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NVIDIA Aerial Omniverse Digital Twin<\/p><\/div><\/p>\n
Typically, 5G simulations are based on stochastic channel models such as the 38.901 channel model. However, to see the effects of higher frequencies in specific urban scenarios, deterministic channel models are preferred over statistical models. Ray tracing \u2013 one such deterministic model \u2013 allows researchers to simulate how signals propagate and interact with buildings and other structures. This approach helps pinpoint potential issues and refine communication solutions for densely populated areas. NVIDIA Aerial Omniverse Digital Twin<\/a> is a tool that enables high-performance ray tracing of detailed models of the real world.<\/p>\n
MATLAB can be integrated with NVIDIA Aerial Omniverse Digital Twin (AODT) to comprehensively model a 6G link with a realistic ray traced channel model. With MATLAB handling the 6G signal processing, and AODT offering realistic environment models, the combination results in accurate 6G link-level simulations. The figure below shows the interaction between the two tools.<\/p>\n
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Interaction between MATLAB and AODT<\/p><\/div><\/p>\n
Using raytracing, AODT calculates the signal paths in a specific dense urban scenario and stores the channel frequency responses in a database. After that, we run a detailed link-level simulation in MATLAB, where we replace the traditional 38.901 channel model with the frequency responses imported from the database. This solution enables engineers and researchers to investigate the effects of different design choices, such as potentially new modulation schemes, to see how system performance is affected in a specific geographical location.<\/p>\n
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MATLAB link level simulation using channel response from AODT<\/p><\/div><\/p>\n
In conclusion, integrating MATLAB with NVIDIA AODT creates a framework for 6G research and development. This enables researchers to better understand and solve the challenges associated with urban environments, ensuring robust future communication networks.<\/p>\n
Please share your comments and experiences below!<\/p>\n
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6G will unlock new use cases for wireless cellular networks, such as sensing and building digital twins. To support these innovations, integrating advanced link-level simulations with ray tracing… read more >><\/a><\/p>\n","protected":false},"author":221,"featured_media":209,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/posts\/173"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/users\/221"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/comments?post=173"}],"version-history":[{"count":17,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/posts\/173\/revisions"}],"predecessor-version":[{"id":264,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/posts\/173\/revisions\/264"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/media\/209"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/media?parent=173"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/categories?post=173"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/semiconductors\/wp-json\/wp\/v2\/tags?post=173"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}