<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>LIDAR Archives - CloudRF</title>
	<atom:link href="https://cloudrf.com/tag/lidar/feed/" rel="self" type="application/rss+xml" />
	<link>https://cloudrf.com/tag/lidar/</link>
	<description>Radio planning today</description>
	<lastBuildDate>Mon, 25 Aug 2025 15:18:23 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	

<image>
	<url>https://cloudrf.com/wp-content/uploads/2021/05/CloudRF_logo_70px.png</url>
	<title>LIDAR Archives - CloudRF</title>
	<link>https://cloudrf.com/tag/lidar/</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>GPU propagation engine</title>
		<link>https://cloudrf.com/gpu-propagation-engine/</link>
		
		<dc:creator><![CDATA[CloudRF]]></dc:creator>
		<pubDate>Mon, 17 Jan 2022 14:20:49 +0000</pubDate>
				<category><![CDATA[API]]></category>
		<category><![CDATA[Automotive]]></category>
		<category><![CDATA[Electronic Counter Measures (ECM)]]></category>
		<category><![CDATA[IoT]]></category>
		<category><![CDATA[LPWAN]]></category>
		<category><![CDATA[Modelling]]></category>
		<category><![CDATA[Theory]]></category>
		<category><![CDATA[CUDA]]></category>
		<category><![CDATA[GPU]]></category>
		<category><![CDATA[LIDAR]]></category>
		<guid isPermaLink="false">https://cloudrf.com/?p=11771</guid>

					<description><![CDATA[<p>Our fast GPU engine is perfect for modelling wireless coverage We have developed the next generation of fast radio simulation engines for urban modelling with NVIDIA CUDA technology and Graphics Processing Units (GPUs). The engine was made to meet demand across many sectors, especially FWA, 5G and CBRS for speed and accuracy. As well as [&#8230;]</p>
<p>The post <a href="https://cloudrf.com/gpu-propagation-engine/">GPU propagation engine</a> appeared first on <a href="https://cloudrf.com">CloudRF</a>.</p>
]]></description>
										<content:encoded><![CDATA[
<h4 class="wp-block-heading alignwide has-text-align-center"><strong>Our fast GPU engine is perfect for modelling wireless coverage</strong></h4>



<p>We have developed the next generation of fast radio simulation engines for urban modelling with NVIDIA CUDA technology and Graphics Processing Units (GPUs). </p>



<p>The engine was made to meet demand across many sectors, especially <strong>FWA, 5G and CBRS </strong>for speed and accuracy.</p>



<p>As well as fast viewsheds, it enables a new automated <strong>best-site-analysis </strong>capability, which will accelerate site selection and improve efficiency whilst keeping a human in the loop. As we can do clutter attenuation, it&#8217;s suitable for VHF and LPWAN also.</p>



<h2 class="wp-block-heading">Designed for 5G</h2>


<div class="wp-block-image">
<figure class="alignright size-large is-resized"><a href="https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-scaled.jpg" rel="lightbox[11771]"><img fetchpriority="high" decoding="async" width="2100" height="2099" src="https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited.jpg" alt="" class="wp-image-11832" style="width:293px" srcset="https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited.jpg 2100w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-300x300.jpg 300w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-1024x1024.jpg 1024w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-150x150.jpg 150w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-768x768.jpg 768w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-1536x1536.jpg 1536w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-2048x2048.jpg 2048w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-324x324.jpg 324w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-416x416.jpg 416w, https://cloudrf.com/wp-content/uploads/2022/01/Cell-Tower-edited-100x100.jpg 100w" sizes="(max-width: 2100px) 100vw, 2100px" /></a></figure>
</div>


<p class="has-normal-font-size"><strong>5G networks</strong> are much denser than legacy standards due to the limited range of <a href="https://en.wikipedia.org/wiki/Extremely_high_frequency">mmWave</a> signals, necessary for high bandwidth data. The same limitation means these signals are <em>very</em> sensitive to obstructions, and Line of Sight (LOS) coverage is essential for performance.  </p>



<p><strong>With 1 metre accuracy</strong> and support for LiDAR, 3D clutter and custom clutter profiles, you can model rural and urban areas with high precision.</p>



<p></p>



<p></p>



<h2 class="wp-block-heading">We can do Trees too</h2>



<p>Unlike simplistic viewshed GPU tools designed for speed, we can model actual tree attenuation for beyond line of sight sub-GHz signals such as LPWAN and VHF. Trees can be configured as clutter profiles, along with shrubs, swamps, urban areas and 18 classes of Land cover and custom clutter.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><a href="https://cloudrf.com/wp-content/uploads/2023/06/trees-vhf.jpg" rel="lightbox[11771]"><img decoding="async" width="900" height="537" src="https://cloudrf.com/wp-content/uploads/2023/06/trees-vhf.jpg" alt="" class="wp-image-19164" srcset="https://cloudrf.com/wp-content/uploads/2023/06/trees-vhf.jpg 900w, https://cloudrf.com/wp-content/uploads/2023/06/trees-vhf-300x179.jpg 300w, https://cloudrf.com/wp-content/uploads/2023/06/trees-vhf-768x458.jpg 768w, https://cloudrf.com/wp-content/uploads/2023/06/trees-vhf-416x248.jpg 416w" sizes="(max-width: 900px) 100vw, 900px" /></a></figure>
</div>


<h2 class="wp-block-heading has-text-align-left">Area coverage</h2>



<p class="has-text-align-left">The simplest mode is a fast &#8220;2.5D&#8221; viewshed (with a path loss model) which creates a point-to-multipoint heatmap around a given site using LiDAR data. Ours has better Physics than some of the &#8220;line of sight&#8221; eye candy on the market and doesn&#8217;t have trouble with Sub-GHz frequencies which are harder to model accurately.</p>



<p class="has-text-align-left"><strong>This is up to 50 times faster than our multi-threaded CPU engine, SLEIPNIR.</strong></p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="GPU RF propagation engine" width="980" height="551" src="https://www.youtube.com/embed/gBrRfwcIhks?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div><figcaption class="wp-element-caption">GPU demo January 2022</figcaption></figure>



<p>In this mode we can do diffraction and material attenuation with our custom clutter classes.</p>



<h2 class="wp-block-heading has-text-align-left">Best site analysis</h2>



<p>Best Site Analysis (BSA) is a monte-carlo analysis technique across a wide area of interest to identify the best locations for a transmitter. This can be done quickly with a new /bsa API call. The output will identify optimal sites, and just as important, inefficient sites.</p>



<p>This feature is powerful for IoT gateway placement, 5G deployments and ad-hoc networking where the best site might presently be determined by a map study based on contours as opposed to a LiDAR model.</p>



<figure class="wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="Best Site Analysis - Finding the optimal location for a radio in an area" width="980" height="551" src="https://www.youtube.com/embed/S9KaPbQGt8A?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div><figcaption class="wp-element-caption">Best Site Analysis</figcaption></figure>



<h2 class="wp-block-heading">High speed</h2>



<p>Our GPU engine is up to <strong>50 times faster</strong> through the API than the current (CPU) engine <a href="https://cloudrf.com/docs/sleipnir-propagation-engine">SLEIPNIR<sup>TM</sup></a></p>



<p>By harnessing the power of high performance graphics cards, we are able to complete high resolution LiDAR plots in near real time, negating the need for a &#8220;go&#8221; button, or even a progress bar. This speed enables API integration with vehicles and robots which will need to model wireless propagation quickly to make better decisions, especially when they&#8217;re off the grid. It was designed around consumer grade cards like the GeForce series but supports enterprise Tesla grade cards due to our card agnostic design. </p>



<h2 class="wp-block-heading">Economical</h2>



<p>Our implementation is efficient by design. We want speed to model wireless coverage but not if it requires kilowatts of power. During testing we worked with older GeForce consumer cards and were able to model millions of points in several milliseconds with less than <strong>50W of power</strong>. Or in other words, the same power as flicking a light bulb on and off.</p>



<p>Any fool can buy large cards and waste electricity, but we&#8217;re proud to have a solution which is fast <strong><span style="text-decoration: underline;">and economical. </span></strong>This also means it can be run on a laptop as it&#8217;s available now as our <a href="https://cloudrf.com/soothsayer/">SOOTHSAYER</a> product. </p>



<h2 class="wp-block-heading">Open API</h2>



<p>The GPU engine is an &#8220;engine&#8221; parameter in our /area <a href="https://cloudrf.com/api-2-0/">API</a> so you can use it from any interface (or your own <a href="https://github.com/Cloud-RF/CloudRF-API-clients">custom interface</a>) by setting the engine option in the request body.  The <a href="https://cloudrf.com/documentation/developer/swagger-ui/">OpenAPI 3.0 compliant API</a> returns JSON which contains a PNG image so for existing API integrations using our PNG layers there will be no code changes required to enable it.</p>



<h2 class="wp-block-heading">Self-hosted GPU server</h2>



<p>Instead of buying milk every month you can buy the cow. We also sell <a href="https://cloudrf.com/soothsayer/">SOOTHSAYER</a> which is a self-hosted server with our GPU engine onboard. You get to use your existing LiDAR data too, so you&#8217;re not buying it twice.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full is-resized"><a href="https://cloudrf.com/wp-content/uploads/2025/08/atak-plugin-2.3-1000px.jpg" rel="lightbox[11771]"><img loading="lazy" decoding="async" width="1000" height="668" src="https://cloudrf.com/wp-content/uploads/2025/08/atak-plugin-2.3-1000px.jpg" alt="" class="wp-image-51365" style="width:626px;height:auto" srcset="https://cloudrf.com/wp-content/uploads/2025/08/atak-plugin-2.3-1000px.jpg 1000w, https://cloudrf.com/wp-content/uploads/2025/08/atak-plugin-2.3-1000px-300x200.jpg 300w, https://cloudrf.com/wp-content/uploads/2025/08/atak-plugin-2.3-1000px-768x513.jpg 768w, https://cloudrf.com/wp-content/uploads/2025/08/atak-plugin-2.3-1000px-416x278.jpg 416w" sizes="auto, (max-width: 1000px) 100vw, 1000px" /></a></figure>
</div>


<p> </p>



<p></p>
<p>The post <a href="https://cloudrf.com/gpu-propagation-engine/">GPU propagation engine</a> appeared first on <a href="https://cloudrf.com">CloudRF</a>.</p>
]]></content:encoded>
					
		
		
			</item>
	</channel>
</rss>
