5G will bring new unique network and service capabilities. Firstly, it will ensure users experience continuity in challenging situations such as high mobility (e.g., in trains), very dense or sparsely populated areas, and journeys covered by heterogeneous technologies. In addition, 5G will be a key enabler for IoT by providing a platform to connect a massive number of sensors, rendering devices and actuators with stringent energy and transmission constraints.
5G networks use the concept of end-to-end network slicing, which enables the concurrent deployment of multiple end-to-end logical, self-contained and independent shared or partitioned networks on a common infrastructure platform, to achieve the performance and scalability requirements. Recursive network slicing, i.e., slices overlaid on top of other network slices, is also supported.
To manage such highly scalable and recursively sliced 5G networks and to maintain the customer QoE and SLAs in real-time, the OSS systems managing the operations must be autonomous and self-driven. It should be AI/ML-based and must support cognitive algorithms for automation of network operations.
Altran proposes a Reinforcement Learning based Recursive Autonomic OSS Solution for operating the 5G networks. The proposed solution is analogous to the concept of autonomic control systems and reinforcement learning present in the human body. Altran’s proposed OSS solution uses the recursive model for reinforcement learning to achieve the required scalability and efficiency.
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