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Master Thesis: Machine learning based Real-time call-drop Healing in 5G networks

Job Description

Date: Nov 8, 2019

Background
Data call drop in mobile networks have been traditionally solved by KPI analysis and changing thresholds based on descriptive analytics. In networks of the future we expect this to be self-healed in real-time using machine learning techniques. Thus resulting in operationally autonomous and efficient networks which adapt to external influence  by continuously learning and improving in a data-driven context. Taking a step towards the above vision this proposal proposes a real-time healing mechanism for call drop scenarios.

Objective

1. Get measurement metrics which will include source and target gNB: RSRP for two scenarios:
• MetricA (Ideal call scenario) = { RSRPservingCell, RSRPtargetCell }
• MetricB (Bad-call quality/Call drop) = { RSRPservingCell, RSRPtargetCell }
2. The HEALING sub-system derives the deviations using machine learning algorithm.
3. The deviations are then gradually applied in the form of delta RSRP or antenna tilt.
4. After each cycle of action, we intend to make the system tends towards target performance i.e. ideal call scenario. That when healing is achieved.

 
Qualifications

  • We are searching for one or two students with fundamental understanding of wireless networks  with some basic understanding of  5G /NR, and beamforming ramework in 5G
  • Additionally  high level understanding of Machine learning will  be  a good add-on
  • Working knowledge of Matlab and Python is essential
  • This proposal is to achieve healing in 5G gENBs by detecting the deviations in UE reported RSRP using machine learning (comparing ideal and dropped call RSRPs) followed by applying  correct RSRP/antenna tilt to heal the call drop in real-time

 

Extent: 2 student
Work location: Lund
Preferred Starting Date: Q1 2020
Last Day to Apply: 24th of November
Contact: Agnes Westerberg,
agnes.westerberg@ericsson.com

 

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Primary country and city: Sweden (SE) || || Lund || Stud&YP

Req ID: 303678