{"id":9065,"date":"2024-11-11T06:43:00","date_gmt":"2024-11-11T06:43:00","guid":{"rendered":"https:\/\/www.gsma.com\/get-involved\/gsma-foundry\/?post_type=gsma_theme_resources&p=9065"},"modified":"2025-09-19T12:23:06","modified_gmt":"2025-09-19T12:23:06","slug":"automating-traffic-balancing-across-cells-ericsson","status":"publish","type":"gsma_theme_resources","link":"https:\/\/www.gsma.com\/get-involved\/gsma-foundry\/gsma_resources\/automating-traffic-balancing-across-cells-ericsson\/","title":{"rendered":"Ericsson: Automating traffic balancing across cells"},"content":{"rendered":"\n

Challenge:<\/strong> When individual base stations are overloaded by traffic, the ensuing congestion impacts the network performance and the end-user experience. Mobile operators need to be able to predict when base stations will become overloaded so they can proactively hand over traffic from the affected cell to neighbouring cells.<\/p>\n\n\n\n

Solution:<\/strong> Ericsson<\/a> has developed an AI-based solution to predict traffic-related key performance indicators (KPIs) for each cell for the next hour. Based on the temporal structure of these KPIs, the solution employs a configurable rule-based classification module to identify so-called victim cells. It then hands over traffic from these victim cells using A3\/A5 offset mechanisms to neighbouring cells.  The solution is designed to be agnostic to cell and band addition\/deletion. <\/p>\n\n\n\n

Impact:<\/strong> The solution is designed to boost overall traffic throughput, thereby improving the end-user experience. It also makes greater usage of the available network capacity and spectrum resources. <\/p>\n\n\n\n

Topic:<\/strong> Network optimisation, and predictive and analytic AI<\/p>\n\n\n\n

Cost:<\/strong> Medium<\/p>\n\n\n\n

MNO:<\/strong> Ericsson<\/p>\n\n\n\n

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