The answer lies with big data powered, smart network automation. P.I. Works uSON (Unified Self Organizing Networks), the industry leading mobile network planning, management and optimization platform, moves beyond radio access network and offers capabilities that span the entire mobile network. The uSON product combines the power of centralized business automation with application level CEM KQIs to move the end-user-experience to next level.
With cognitive cell profiling mechanism based on mobile network and Customer Experience Management (CEM) data, uSON determines the optimum set of optimization actions in a comprehensive way. It focuses on customer experience metrics to enhance mobile user experience while driving operational efficiency.
CEM data is also used to take targeted network management actions tailored to specific subscriber groups. This redefines the network optimization techniques from network element based optimization to service and segment centric optimization. For example, P.I. Works uSON solution allows operators to utilize customer segment data coming from CEM systems to shape their problem identification methodologies and solution sets. This approach further improves the quality of experience of these customer groups without compromising on the experience of the remaining subscribers. P.I. Works solution also factors in other data points recorded in the CEM system, such as device profile and penetration figures as well as video/data performance. Service and segment aware centralized SON operation opens a new era in network management by enabling slice centric optimization for IoT, MBB, URLLC, V2X services to help mobile operator achieve service SLAs.
P.I. Works uSON, with its centralized SON and statistical learning capabilities, quickly identifies the network problems and takes automated actions. For example, in case of a network outage in a specific site where premium customers are heavily present, P.I. Works uSON’s Intelligent Cell Outage Detection and Compensation module takes corrective actions by utilizing the capacity of the neighbor cells.
Another key aspect of the solution is service aware driveless coverage optimization and automated service quality tracking for VIP routes. With this approach, coverage issues are identified, treated and verified not only from the network counter Key Performance Indicators but also from the CEM Key Quality Indicators (e.g., time not on LTE and video stalling time). Combining the machine learning based clustering algorithms with closed-loop optimization process guarantees the best possible service quality in heterogeneous mobile networks. Utilization of internal or external call-traces and the geolocation engine outputs allows operators to proactively identify VIP route locations in a secure and anonymous way. Based on this data operators can run automated virtual drive-tests in order to proactively detect quality problems. Such smart VIP experience management process does not only improve the network-wide service quality and saves operational costs, but also reduces the carbon footprint as costly and time-consuming drive tests are avoided.
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