Mobile Station Session Drop Analysis via Automated Metrics
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Solution Overview
Problem
Current network optimization techniques for wireless networks are manual and time-consuming, lacking automation to identify and address session drops effectively, which impedes the recognition and evaluation of optimization needs.
Innovation Solution
A system and method that automatically collects and analyzes communication metrics from mobile units to determine the cause of session drops, categorizing them to direct attention to the correct optimization activities, using probability metrics to identify issues such as poor coverage, interference, hardware performance, network congestion, and core malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual network optimization techniques are used to identify session drop causes, then service providers can analyze network issues, but the process is time-consuming and lacks automation
Solution Approach 1:
The system enables self-service by having mobile stations automatically collect and report communication metrics to the base station, which then automatically analyzes the data to determine session drop causes. This eliminates the need for manual service provider intervention in data collection and initial analysis, significantly reducing the time required for network optimization while maintaining high automation levels.
2Measurement precision
If comprehensive communication metrics are collected from mobile units to accurately determine session drop causes, then analysis precision improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of session drop analysis by dividing it into distinct functional components: mobile stations collect specific communication metrics (RSSI, CINR, mobile transmit power), base stations receive and organize this data, and then analyze it to determine causes. This segmentation allows each component to handle a specific aspect of the problem, maintaining measurement precision while managing system complexity through modular architecture.
3Productivity
If automated systems collect and analyze communication metrics from multiple mobile units, then productivity of network optimization improves, but device complexity increases
Solution Approach 1:
The base station serves multiple functions: it acts as a communication interface with mobile stations, collects communication metrics from multiple sources, stores the metrics data, and performs analysis to determine session drop causes. This multi-functionality consolidates what could be separate complex systems into a single integrated platform, improving productivity by enabling automated analysis across the network while managing device complexity through functional integration.
Data Source
AI summary
A mobile station (MS) used in a wireless communication system collects communication metrics data during a call session and stores the communication metrics data in the MS at the time that a session is disrupted. When a new communication link is established, the MS transmits the stored communication metrics. The communication metrics and other data relating to the disrupted communication session may be analyzed to determine which communication metrics were operating at abnormal values and determine the probability that a particular communication metric was related to the cause of the session disruption. The communication metrics values and probability metrics values may be used to determine a likely cause for the session disruption.