Wireless Network Parameter Conflict Resolution
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Solution Overview
Problem
Current network planning and optimization for wireless networks are manual, resource-intensive, and infrequent, leading to suboptimal resource utilization, revenue loss, and degraded service quality due to the lack of automated systems that can dynamically adjust radio resources and resolve conflicts in network parameter modifications.
Innovation Solution
An automated system that calculates and stores modifications to network parameters, identifies conflicts, and optimizes cell sites or sectors by creating new modification groups to improve wireless network performance, while also determining the best previous modifications to address degraded performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual network planning and optimization is performed periodically, then network performance can be improved at specific intervals, but the network remains suboptimal for long periods between implementations, resulting in revenue loss and degraded service quality
Solution Approach 1:
The system implements self-service through automated monitoring and optimization capabilities. The network system automatically detects performance degradation, identifies critical zones, calculates optimal modifications, and implements improvements without requiring manual intervention at each step, enabling continuous optimization rather than periodic manual adjustments
Solution Approach 2:
The system ensures continuity of useful action by continuously monitoring network performance metrics, maintaining a modification queue with pending optimizations, and automatically implementing improvements as conditions warrant. This eliminates the gaps between periodic manual optimizations, keeping the network continuously optimized rather than allowing long periods of suboptimal performance
2Productivity
If multiple modifications to network parameters are implemented simultaneously, then optimization can be achieved faster, but conflicts between modifications for the same parameter can arise, delaying the application of appropriate recommendations
Solution Approach 1:
The system segments modifications into organized groups with hierarchical relationships. Modification groups contain individual modifications, and parent groups coordinate child groups to ensure consistent parameter changes. This segmentation allows simultaneous implementation of multiple modifications while preventing conflicts through structured organization and coordination mechanisms
Solution Approach 2:
The system introduces an intermediary evaluation mechanism that assesses modification groups before implementation. This intermediary process checks for conflicts between modifications, validates parameter changes, and coordinates the application of multiple modifications to ensure they work together harmoniously, preventing delays caused by unresolved conflicts
3Productivity
If automated systems continuously optimize network parameters, then resource utilization is maximized and service quality is enhanced, but the system complexity and computational resources required increase
Solution Approach 1:
The system implements dynamic optimization by continuously adapting to changing network conditions. It monitors performance metrics in real-time, adjusts modification priorities based on current state, and implements changes dynamically without requiring complete system reconfiguration. This dynamic approach maximizes resource utilization while keeping system complexity manageable through adaptive rather than static optimization
Solution Approach 2:
The system applies partial action by focusing optimization efforts on critical zones where performance degradation is detected, rather than attempting to optimize the entire network uniformly. This targeted approach improves resource utilization efficiency in areas that need it most while reducing the overall computational complexity and system resources required for continuous optimization
Data Source
AI summary
Optimizing a plurality cell sites or sectors in a wireless network includes calculating modifications to a plurality of network parameters for optimizing wireless network performance; evaluating the modification groups to determine conflicts between modifications for a same network parameter; and eliminating the conflicts between modifications for the same network parameter within the modification groups. The modification groups are used to alter at least one network parameter of the critical cell sites or sectors, or of a best neighbor cells sites or sectors for achieving a desired improvement in communications within the wireless network. Optimization is further enhanced by determining the best previous modifications to the wireless network when performance continues to be degraded. Altering wireless network parameters of the critical cell sites or sectors, or the best neighbor cell sites or sectors is performed continuously using the stored modification groups until the desired improvement in communications in the wireless network is achieved.