Cell Operational Priority Index for Network Optimization
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Solution Overview
Problem
Existing wireless communication systems lack efficient mechanisms for dynamically prioritizing and optimizing network operations based on cell priorities, leading to suboptimal maintenance activities and energy usage, especially in complex scenarios like natural calamities or high traffic conditions, without considering dynamic network characteristics.
Innovation Solution
Introduce a cell operational priority index (COPI) to automate cell prioritization, enabling network optimization functions like NOWOA and NAAP to optimize workflows, exclusion lists, and energy scheduling based on real-time or predicted cell priorities, using analytics for traffic and revenue data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic cell prioritization is implemented, then network optimization efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments network optimization into distinct functional modules: priority value determination module, workflow sequence determination module, exclusion list determination module, and energy saving scheduling module. Each module handles specific optimization tasks independently, improving overall efficiency while managing complexity through modular architecture.
Solution Approach 2:
The system implements dynamic cell prioritization by continuously determining priority values based on current network conditions, traffic patterns, and operational requirements. This dynamic approach enables the system to adapt to changing conditions and optimize performance in real-time without requiring complex manual reconfiguration.
2Loss of time
If manual maintenance scheduling is used, then operational control is maintained, but maintenance downtime increases
Solution Approach 1:
The system performs preliminary actions by determining optimal workflow sequences and identifying suitable maintenance windows in advance. The workflow sequence determination module analyzes network conditions and schedules maintenance activities during periods of lowest impact, thereby reducing actual maintenance downtime while maintaining operational control through planned automation.
Solution Approach 2:
The system incorporates feedback mechanisms where the management system monitors network performance and maintenance outcomes, then adjusts future scheduling decisions accordingly. This feedback loop enables the automated system to learn from past operations and continuously improve its scheduling accuracy, reducing downtime while maintaining control.
3Loss of energy
If energy saving scheduling is implemented, then energy consumption is reduced, but network availability may be compromised
Solution Approach 1:
The system applies local quality by differentiating energy saving strategies across individual cells based on their priority levels and operational requirements. High-priority cells maintain full availability while low-priority cells are scheduled for energy saving modes, allowing the system to reduce overall energy consumption without compromising the availability of critical network segments.
Solution Approach 2:
The energy saving scheduling module dynamically adjusts network availability settings based on real-time priority values and traffic conditions. During low-traffic periods, the system can schedule energy saving modes for non-critical cells, while automatically restoring full availability when priority levels change or traffic patterns indicate increased demand, thus balancing energy efficiency with network reliability.
Data Source
AI summary
There is provided an apparatus comprising means for: receiving, from a management system, information indicating one or more priority values associated with a respective one or more cells; and performing one or more network optimization processes based on the received information indicating the one or more priority values associated with the respective one or more cells.