AI-RRM Auto-Pilot for Wireless Configuration Rollout
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Solution Overview
Problem
The manual application of configuration recommendations in wireless networks is time-consuming and inconvenient, often leading to network downtime and inefficient use of wireless spectrum due to radio interference and co-channel interference, which existing RRM systems struggle to address effectively.
Innovation Solution
An auto-pilot functionality integrated with AI-RRM that automatically applies configuration recommendations based on user-defined conditions and policies, including timing, zones, and network health, reducing the need for manual intervention and optimizing network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual application of configuration recommendations is used, then network performance can be optimized, but the process is time-consuming and causes network downtime
Solution Approach 1:
The system enables self-service by allowing the wireless network to automatically apply configuration recommendations without manual administrator intervention. The auto-pilot functionality autonomously receives RRM recommendations, evaluates them against policies, and applies configurations automatically, eliminating the need for human operators to manually intervene in routine optimization tasks.
Solution Approach 2:
The system performs preliminary action by pre-defining configuration policies, timing schedules, and application conditions before network optimization is needed. Administrators set up policy frameworks in advance that specify when and how configurations should be applied, allowing the system to execute optimized configurations automatically without requiring real-time decision-making during network operations.
2Loss of time
If configuration recommendations are applied automatically, then network downtime is minimized, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the complex configuration application process into distinct manageable segments: policy definition phase, recommendation reception phase, evaluation phase, and application phase. Each segment is handled by a dedicated functional component, making the overall complex system more modular and easier to administer despite the automation capabilities.
3Reliability
If RRM analyzes RF environment continuously, then network performance is optimized, but processing resources are consumed
Solution Approach 1:
The system applies partial action by selectively analyzing and applying RRM recommendations only when necessary rather than continuously processing all possible configurations. The auto-pilot functionality evaluates recommendations against predefined policies and timing constraints, applying configurations only when conditions are met, thereby reducing unnecessary processing resource consumption while maintaining network optimization.
Data Source
AI summary
A system and method are provided for automatically applying configuration recommendations to a wireless network. Configuration recommendations are received from an artificial intelligence (AI) radio resource management (RRM) recommendation engine, and an auto-pilot functionality automatically applies some (or all) of the recommendations to the network, reducing the network administrator's workload. The wireless network receives user inputs for timing, RF frequencies, zones (e.g., building and RF groups therein), risk tolerances, etc. that control configuration policies for how the configuration recommendations are automatically applied to the wireless network. User inputs can be received through a GUI that organizes the types of configuration recommendations into sets (or categories) and associates these sets with respective conditions controlling if/when the respective sets are automatically applied to the network. In addition to facilitating user inputs, the GUI also provides user feedback by reporting on the configuration recommendations received and implemented by the auto pilot.