Adaptive Wireless Network Monitoring via Baseline Deviation Analysis
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Solution Overview
Problem
The complexity and size of wireless communication networks make it difficult to monitor them efficiently, requiring a high degree of reliability and availability, but existing monitoring methods struggle to adapt dynamically to changes in network operations.
Innovation Solution
An adaptive monitoring method that analyzes historical network data to establish baselines for operating characteristics, using call detail records to determine if these characteristics deviate from normal behavior, and dynamically adjusts monitoring operations performed by a network management system to address any deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used on large-scale wireless networks, then monitoring coverage can be comprehensive, but monitoring efficiency and resource usage deteriorate due to network complexity
Solution Approach 1:
The monitoring system dynamically adjusts its operation by transitioning between different monitoring modes (continuous, periodic, event-triggered, idle) based on real-time network conditions. This dynamic adaptation allows the system to maintain comprehensive monitoring coverage when needed while reducing resource consumption during normal operations, directly resolving the contradiction between reliability and productivity.
Solution Approach 2:
The system changes monitoring parameters such as sampling rates, threshold values, and monitoring intensity based on network state analysis. By adjusting these parameters dynamically, the system achieves both comprehensive monitoring coverage and improved efficiency, resolving the contradiction between thorough monitoring and resource usage.
2Reliability
If continuous monitoring is performed on all network elements, then network reliability is improved, but resource consumption and system complexity increase
Solution Approach 1:
The monitoring system is segmented into different operational modes and applies different monitoring strategies to different network elements based on their importance and state. Critical elements receive continuous monitoring while less critical elements use periodic or event-triggered monitoring, reducing overall system complexity while maintaining reliability.
Solution Approach 2:
Instead of continuously monitoring all network elements equally, the system applies partial monitoring actions only when and where needed. The adaptive monitor selectively intensifies monitoring on specific elements or time periods based on detected anomalies or network conditions, reducing complexity while preserving reliability.
3Measurement precision
If monitoring operations are increased to detect network anomalies, then detection precision is improved, but resource usage and false alarm rate worsen
Solution Approach 1:
The system uses feedback from baseline comparisons and anomaly detection results to dynamically adjust monitoring intensity. When anomalies are detected, the system increases monitoring precision and resource allocation to that specific area. When network conditions are normal, monitoring resources are reduced, optimizing the balance between detection precision and resource consumption.
Solution Approach 2:
The system performs preliminary actions by establishing baselines and detection thresholds in advance using historical data. This preliminary preparation enables the system to quickly respond to actual anomalies with precise monitoring only when needed, avoiding continuous high-resource consumption while maintaining high detection precision.
4Productivity
If adaptive monitoring is implemented to optimize resource usage, then productivity is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The adaptive monitor is designed as a universal component that can handle multiple monitoring modes, data types, and network elements through a single integrated architecture. This multi-functionality reduces the need for separate specialized systems, making the complexity manageable while achieving high productivity through adaptive resource optimization.
Data Source
AI summary
Various embodiments provide adaptive monitoring of a wireless communication network. In one embodiment, a first set of network data generated for a wireless communication network is analyzed. The first set of network data is a set of historical network data for the wireless communication network. A baseline for at least one operating characteristic associated with the wireless communication network is determined based on the analyzing. A second set of network data generated for the wireless communication network is received. The second set of call detail records that has been received is utilized to determine if the at least one operating characteristic corresponds to the baseline. A set of monitoring operations performed by a network management system with respect to the wireless communication network is dynamically adjusted based on the at least one operating characteristic failing to correspond to the baseline.