Network Planning Tool Automates Attribute Learning
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Solution Overview
Problem
Network planning for mesh communication networks is time-consuming and requires frequent manual updates to optimize network attributes, making it challenging to balance protection and cost-effectiveness, especially in environments with high susceptibility to fiber cuts like India.
Innovation Solution
A network planning tool that automatically learns and sets network attributes based on predetermined criteria, such as frequent selections or events, to streamline the planning process and reduce designer oversight, including parameters like regeneration sites, add/drop channels, and routing tendencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual updates of network attributes are performed frequently to optimize network planning, then network optimization and protection are improved, but time consumption and operational complexity increase
Solution Approach 1:
The network planning tool automatically learns network attributes by monitoring user selections and events, updating attributes without requiring manual operator intervention. The system serves itself by acquiring knowledge from its own operation patterns, thereby improving network protection while eliminating time-consuming manual updates
Solution Approach 2:
The system implements feedback mechanisms by monitoring user selections, events, and network performance data. This feedback is processed to automatically adjust network attributes, creating a closed-loop system that continuously optimizes network protection based on actual operational patterns and requirements
2Reliability
If manual updates of network attributes are performed frequently to optimize network planning, then network optimization is improved, but operational complexity increases
Solution Approach 1:
The system automatically performs network optimization by learning from operational patterns and autonomously updating network attributes. This eliminates the need for operators to manually adjust complex parameters, thereby improving network optimization while reducing operational complexity
Solution Approach 2:
The network planning tool acts as an intermediary between network operations and optimization decisions. It translates operational patterns into optimized network attributes through automated learning, shielding operators from complex manual adjustments while achieving superior network optimization
3Adaptability or versatility
If network attributes are manually adjusted to meet demand requirements, then network adaptability is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary learning during normal network operations, accumulating knowledge about network patterns and requirements. When adaptation is needed, this pre-acquired knowledge enables rapid attribute adjustment, thereby improving network adaptability while maintaining high planning efficiency
Solution Approach 2:
The automated learning process operates continuously in the background during normal network operations, constantly gathering and processing information. This continuous useful action ensures the system is always ready to adapt quickly to changing requirements without interrupting network operations or reducing productivity
Data Source
AI summary
A method, apparatus, system, and computer program, for operating a network planning tool. The method includes recognizing at least one predetermined criteria being satisfied or triggered, and learning at least one network attribute, to be used in subsequent network planning. Various types of network attributes can be learned, such as, e.g., network element parameters, site parameters, link parameters, demand parameters, optical parameters, general parameters, and networking parameters. Example attributes may specify, e.g., a maximum number of add/drop channels, at least one alarm threshold, a maximum number of sites per network ring, a maximum number of add/drop sites per ring, and a maximum ring circumference. Others can specify a maximum light path distance, a routing tendency, at least one grooming node, or the like.