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IEEE Final Year Project Topics for CSE

Base Paper Title

A supervised learning based QoS assurancearchitecture for 5G networks

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IEEE Project Abstract

The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely lowlatency, and extraordinary high-speed data transfer. The challenge of 5G is to assure the network performance and differentQuality of Service (QoS) requirements of different services such as Machine Type Communication (MTC), enhancedMobile Broad Band (eMBB), Ultra-Reliable Low Latency Communications (URLLC) over 5G networks. Unlike theprevious 'one size fits all' system, the softwarization, slicing and network capability exposure of 5G provide dynamicprogramming capabilities for QoS assurance. With the increasing complexity and dynamics of the network behaviors, it isnon-trivial for programmer to develop traditional software codes to schedule the network resources based on expertknowledge, especially when there is no quantitative relationship among the network events and the QoS anomalies.Machine learning is a computer technology that gives computer systems the ability to learn with data and improveperformance and accuracy of decision making on a specific task, without being explicitly programmed. The areas ofmachine learning and communication technology are converging. A supervised learning based QoS assurance architecturefor 5G networks was proposed in this paper. The supervised machine learning mechanisms, can intelligently learn thenetwork environment and react to dynamic situations. They can learn from the fore passed QoS related information andanomalies, and further reconstruct the relationship between the fore passed data and the current QoS related anomaliesautomatically and accurately. They, then, can trigger automatic mitigation or provide suggestions. The supervised machinelearning mechanisms can also predict future QoS related anomalies with high confidence. In this paper, a case study forQoS anomaly root cause tracking based on decision tree was given to validate the proposed framework architecture.

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