Automated silent eNode B detection

Identifying hidden problems for a Japanese operator

Silent eNB & KPI degradation detection using machine learning algorithms for improved efficiency and network quality.

Silent eNB (or Sleeping Cell) is a well-known problem that affects network equipment from all vendors.

  • The base station is unable to carry traffic resulting in a service degradation but appears to be functioning normally > No Alarm is triggered!
  • Identifying the issue and detecting the root cause was a very manual, time-consuming process for the customer: taking up to 24 hours after the event and only yielding an 80% success rate

Nokia Solution:

  • Use machine learning algorithms to identify and detect the root cause
  • 6 month Proof of Concept in Tokyo area to prove Nokia solution and fine-tune algorithms
  • Solution created following lean software development principles
  • Designed to meet traditional Japanese quality standards: 100% accurate detection and reporting in Near-Real-Time (less than 30 minutes)

Outcome & benefits:

  • 100% success rate for detection of Silent eNodeB, within 30 minutes
  • In addition to identification of silent eNB cases & several hardware faults were also detected
  • Automatic root cause analysis generated to help resolve issues
  • No end user complaints received during the PoC period
  • Proved to be much more effective & efficient than existing customer process
  • Results acknowledged by customer and PO received for full nationwide service covering Nokia, Ericsson & Samsung

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