Date: 09/17/2024
Vendor: Ansys
Technology/Topic: Modeling Contested and Congested Communications in Dense Urban Environments
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Welcome to the Technical Exchange Meeting (TEM)!
Modern 5G and 6G wireless networks operate over wide bandwidths and share congested high frequency bands, which makes it difficult to predict coverage and mitigate interference issues. In addition, mission planners must consider the physical environment, mobile and temporary obstacles within the environment, dynamic user behavior, environmental conditions, regulatory constraints, budget limitations, interference and other factors when evaluating alternatives. Mission planners need tools that can accurately account for all of these real-world factors. Ansys has created an effective simulation solution for this challenge called the RF Channel Modeler (RFCM).
Learn how to employ a model-based design approach to represent your systems within a physics accurate digital environment. Designs can be explored and tested across simulated situations and conditions which would otherwise be difficult to replicate under real-world field-testing scenarios by following a streamlined modeling and simulation workflow:
— Modeling the Operating Environment
— Platform Models
— Analyze and Explore
Existing technology has not been able to support the wideband, multi-phase channel model requirements of 5G/6G. Further, these channel models require complex frequency and time domain characterization at high rates, which in the past would have driven highly excessive physics simulation capacity. RFCM is GPU accelerated which enables extremely fast and accurate calculations. RFCM allows engineers to model high-fidelity, wireless 5G and 6G communications networks in accurate bespoke virtual design spaces that replicate real-world locations, including local terrain, building data, materials, and existing RF infrastructure. Engineers can explore alternative configurations and perform parametric analysis to quickly identify optimized performance solutions. This is an analysis paradigm shift from managing and saving massive data sets to managing run-configurations to generate data on-demand.
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