# Introduction to HeavyRF HeavyRF is a radio frequency propagation simulation that supports a variety of use cases, including coverage mapping and customer-based metrics like quality of service (QoS). HeavyRF uses a physically based approach, casting rays from antenna locations and tracing their interactions with several types of "clutter". Clutter is any obstacle that has a significant effect on radio frequency propagation, including terrain, vegetation, and buildings. These can be imported from various sources, including vector GIS boundary representations with height attributes, raster representations, or classified LiDAR point clouds. {% hint style="info" %} HeavyRF is a separately licensed module for the HEAVY.AI Enterprise platform. {% endhint %} The output of this simulation is a set of discrete pixels or voxels, represented as points with RF properties. You can control the resolution of these voxels. Signal strength is estimated at each output location moving away from an antenna position. There are three stopping conditions; a ray can be blocked by an object, attenuated below a minimum strength you configure, or can reach a specified maximum length. In the simplest version of the simulator, only the maximum strength signal for a location is retained. A “top-n” variant is also available in which the “n” strongest signals are stored, which can be useful, for example, in the computation of interference between antennas. You can use HeavyRF 5G planning where 5G signal strength and propagation is dependent on line of sight and clutter. The HeavyRF solution allows you to compute very large amounts of RF propagation and clutter data quickly and visualize affected regions. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.heavy.ai/heavyrf/introduction-to-heavyrf.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.