Project Description

Incelligent addresses some fundamental challenges of modern wireless networks. Complexity and costs of wireless networks are surging. Long – standing trends and forecasts show that costs are increasing faster than revenues. Traffic demand and volatility lead operators to a dilemma between over-investment or accept competitive disadvantages / challenged customer experience. In many cases operators end up missing all aforementioned goals. Moreover, the network management complexity gradually exceeds the levels that traditional working models can address.

Some automated solutions have emerged [e.g. self-organizing networks (SONs)], but they are not enough for many different traffic situations, or cannot manage networks of numerous interrelated or even overlapping cells, in real time and with the needed reliability.

Incelligent goes further, combining existing edge technologies with learning automations and knowledge management techniques, in order to reach faster, automated, predictive, and more reliable decisions with respect to the allocation of resources (e.g., power, spectrum, etc.), selection of cells (e.g. traffic steering) and radio access technologies (parallel use of licenses and unlicensed spectrums). It converts legacy and emerging Wireless Mobile broadband networks to intelligent, automated, agile, cost- and energy-efficient systems with potential for much higher revenue. It is real-time, i.e., acts dynamically during the system operation, and it is knowledge-based because it exploits acquired contextual data and previously made decisions, in order to instantly match the best possible solutions to situations, which are encountered.

Intelligent makes wireless broadband networks friendlier to environment and sustainable, while providing better services to end users with less hassle for network operators.