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As consumers, we see it in the downloads that seem to take forever or the pages that won’t load. While consumers bemoan a less than ideal level of service, wireless network providers face increased operational expenses, infrastructure investment, mounting energy costs, and competition that drives revenue down.

For the three-person founding team of Incelligent, the challenge and the alternative were clear. The Athens, Greece-based Startup Focus member revolutionizes wireless network management by leveraging available big data and algorithms to provide telecomm providers with information they can use to optimize network configuration to enhance service quality, minimize cost, and energy consumption.

The algorithms combine machine learning that recognize patterns, predict reappearance patterns, and filter out insignificant variables in order to continually adjust the state of the network and enable real-time automation to reliably handle occurring events. The resulting technology transforms wireless networks into smart, predictive systems and proactively adapts them to usage and mobility patterns, elevating the customer experience and increasing operator profits.

Incelligent usecase

Incelligent’s value add proposition generates value for a host of vendors, from mobile network operators, WiFi managers, equipment vendors, smart city managers, and M2M applications. Typical addressable hotspots range from commercial centers, business centers, transportation exchanges like airports and train stations, stadiums, and conference venues.

incelligent use case

In Incelligent’s first alpha study of a 4G served high traffic metropolitan train station, the technology utilized a knowledge-based SON function that processes the predicted load to schedule future actions to be taken on the pico-cells in order to offload the respective macro cells that cover the same area.

The key advantage of this approach, is that the SON function has the ability to prepare the network shortly before the predicted phenomenon takes place, thus leading to less handovers and greater stability. Moreover, the added value of the approach is twofold: on the one hand the power (and thus energy) consumption of the network is reduced by 18 percent (a savings of approximately 4 kilowatt-per-hour per macro cell per day due to the more efficient utilization of cell capacity in time and space, while the quality of service offered to the user is also enhanced due to faster download (less average file download session duration by 68 percent).

The startups is in discussion with the main vendors of wireless broadband technologies and are in the process of establishing additional pilots in collaboration with local area operators using current technology.

SAP HANA + Incelligent

Incelligent is inherently based on the analysis and exploitation of big network, customer and other data both for providing analytics and insights towards the user, be it a mobile network, WiFi, or smart city operator and for rapidly proposing and enforcing reliable reconfiguration actions towards the network. In addition, the platform provides the primary database and warehousing and offers fast, real time, cloud-based processing capabilities to Incelligent’s solution, enabling rapid proof of concept and transitioning to a scalable prototype in a very short span of time.

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