Automate and Optimize Network Planning with Eino’s AI Technology

Founded in 2019 by CEO Payman Samadi, Eino.ai is at the forefront of utilizing artificial intelligence (AI) to revolutionize network planning. The company’s innovative platform incorporates digital twins, AI-assisted design, and validation capabilities to transform the way networks are designed and deployed.

The journey commences with the creation of accurate digital twins of the environment. Samadi explained during a presentation in Silicon Valley: “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.”

However, the creation of digital twins is just the beginning. Eino.ai then utilizes AI to optimize 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 addresses various use cases by incorporating specific coverage, capacity, and interference criteria.

“For example, in a warehouse where we have a lot of metal shelves in between, there should be an algorithm that is capable of adjusting based on that,” Samadi explained.

Upon completion of the AI-assisted design, validation becomes essential. Samadi highlighted that constructing a network and collecting data often pose challenges, as the collected data may not be as detailed as the design data. Eino.ai aims to automate this labor-intensive process.

The platform’s power is exemplified 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 elaborated on the indoor WiFi example.

For the outdoor cellular use case, demand mapping enables the AI to tailor the design. Samadi noted that there were three different areas with high demand due to autonomous devices, while other areas had lower demand.

In the fixed wireless demonstration, terrain data is used to analyze line-of-sight. Samadi remarked, “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 utilize digital twins and AI-driven design automation to implement optimized networks for various use cases.

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In conclusion, Eino.ai represents a groundbreaking approach to network planning by integrating artificial intelligence and digital twins. The platform’s ability to automate and optimize the design process sets it apart from traditional methods, allowing for more efficient and effective network deployment. With Eino.ai, the future of network planning looks promising as companies can harness the power of AI to create advanced and tailored network solutions for diverse use cases.