HomeMetaverseEino Style: Utilizing AI for Streamlining & Enhancing Network Planning

Eino Style: Utilizing AI for Streamlining & Enhancing Network Planning

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Founded in 2019 by CEO Payman Samadi, Eino.ai is pioneering the application of artificial intelligence (AI) to automate and optimize network planning. Its cutting-edge platform integrates digital twins, AI-assisted design and validation capabilities to revolutionize how networks are designed and deployed.

The journey begins with constructing accurate digital twins of the environment. As Samadi explained during a Silicon Valley presentation: “We start with an area. If it’s indoor, we have some layouts, we have walls. If it’s outdoor, we have our buildings, obstructions, trees, and everything and this the starting point.”

But creating digital twins is just the first step. Eino.ai then leverages AI to enhance the design process.

“We came up with understanding that where is that complexity,” Samadi said. “You have the coverage problem, you have the capacity problem, you have different types of use cases and demand in different areas.”

The platform tackles diverse use cases by incorporating specific coverage, capacity and interference criteria.

“You have, for example, a warehouse where we have a lot of metal shelves in between so it should be some sort of algorithm that is able to understand and adjust based on that,” Samadi noted.

Once the AI-assisted design is complete, validation is crucial. Samadi explained that building a network and collecting data often presents challenges in comparison, as the collected data may not be as granular as the design data. He noted that Eino.ai aims to automate this labor-intensive process.

The power of the platform is demonstrated through three end-to-end scenarios: indoor WiFi, outdoor private cellular, and fixed wireless design.

“I’ll start with the indoor first. I upload the layout there. It has generated wall functionality from an AI assistant,” Samadi explained of the indoor WiFi example.

Samadi demonstrated the outdoor cellular use case by explaining how demand mapping enables the AI to customize the design. He pointed out that there were three different areas with high demand due to autonomous devices, while other areas exhibited much lower demand.

On the fixed wireless demonstration utilizes terrain data to analyze line-of-sight, Samadi had this to say: “You’ll be able to do line of sight analysis…and then see where you have your line of sight what’s your frontal Zone analysis.”

With Eino.ai, network planners can harness the power of digital twins and AI-driven design automation to deploy optimized networks across diverse use cases.

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