Network Node Configuration Using RF Environment Embeddings
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Solution Overview
Problem
Existing network nodes configured with similar parameters perform differently due to varying RF environments and external variables, leading to suboptimal network performance in terms of throughput, latency, and error rates.
Innovation Solution
An autonomous network optimization agent uses AI/ML to generate a fine-grained representation of the RF environment through satellite and aerial images, integrating spatiotemporal features and external variables to optimize communication parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If network nodes are configured with similar configuration parameters, then device complexity is reduced and ease of manufacture is improved, but network performance varies due to differing RF environments leading to suboptimal throughput, latency, and error rates
Solution Approach 1:
The patent applies local quality by configuring network nodes with environment-specific parameters tailored to their local RF conditions. Instead of uniform configuration, each network node receives customized configuration data based on its specific geographic location, surrounding structures, vegetation, and environmental characteristics, thereby optimizing performance for each local context while maintaining standardized deployment processes
Solution Approach 2:
The system dynamically adjusts configuration parameters based on RF environment analysis. By changing parameters such as transmission power, frequency allocation, antenna orientation, and modulation schemes according to local environmental conditions, the system optimizes network performance without requiring complex manual configuration for each node
2Reliability
If autonomous network optimization using AI/ML is implemented, then network performance is improved through optimized configuration parameters, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary analysis of RF environments using satellite imagery, aerial photography, and geographic data before network node deployment or reconfiguration. By pre-characterizing the environmental conditions and pre-computing optimal configuration parameters using AI/ML models, the system reduces the complexity of real-time optimization while maintaining high performance
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between the physical RF environment and network node configuration. This intermediary layer uses AI/ML models to translate environmental characteristics into optimized configuration parameters, shielding network operators from the complexity of manual optimization while delivering performance improvements
Data Source
AI summary
In some implementations, a network device may receive a set of key performance indicators (KPIs) associated with a network node. The network device may determine a KPI embedding associated with the set of KPIs. The network device may receive an image of a radio frequency (RF) environment associated with the network node. The network device may determine an RF environment embedding associated with the image of the RF environment. The network device may determine a joint embedding based on the KPI embedding and the RF environment embedding. The network device may determine at least one configuration parameter for the network node based on the joint embedding.