Self-Configuring Wireless Network Nodes Using Propagation Modeling
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Solution Overview
Problem
Existing wireless network systems require extensive pre-engineering and manual configuration, making them inflexible and costly, unable to adapt to changing environmental conditions or optimize performance across all nodes, leading to suboptimal performance.
Innovation Solution
A self-configuring wireless network system that uses real-time environmental data and propagation modeling to dynamically adjust RF operational parameters across all nodes, enabling automatic and iterative optimization of network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration and extensive pre-engineering work are performed, then network deployment accuracy can be ensured, but deployment time and costs increase significantly
Solution Approach 1:
The system performs preliminary propagation modeling and environmental analysis automatically during the deployment process itself, rather than requiring extensive pre-engineering work. The computing device obtains propagation models and environmental data, then automatically determines optimal operating parameters as nodes are deployed, eliminating the need for lengthy manual design processes while ensuring accurate deployment
Solution Approach 2:
The wireless network nodes automatically configure themselves by receiving optimal operating parameters from the computing device based on real-time environmental data and propagation modeling. Each node self-adjusts its RF parameters without requiring manual configuration by technicians, thereby reducing deployment time while maintaining deployment accuracy through automated optimization
2Device complexity
If manual configuration is used with limited user interface options, then device complexity is reduced, but adaptability to changing environmental conditions deteriorates
Solution Approach 1:
The system continuously monitors environmental conditions and network performance, then uses this feedback to automatically adjust operating parameters across the network. The computing device receives environmental data, re-evaluates propagation models, and redistributes optimized parameters to nodes, enabling the network to adapt to changing conditions without requiring complex manual reconfiguration interfaces
Solution Approach 2:
The system dynamically changes RF operating parameters (such as power levels, frequencies, and modulation schemes) based on real-time environmental conditions and propagation modeling results. This automated parameter optimization allows the network to adapt to environmental changes while keeping the user interface simple, as the system handles complexity through automated parameter adjustment rather than user-friendly configuration options
3Ease of operation
If automated configuration mechanisms are implemented with local inputs only, then ease of operation is improved, but overall network performance deteriorates due to lack of system-wide optimization
Solution Approach 1:
The system merges local environmental data with system-wide propagation models and network performance information to determine optimal parameters for each node. The computing device collects data from multiple sources including distant network nodes, combines this information with local measurements, and generates coordinated parameter sets that optimize overall network performance while maintaining automated operation
Solution Approach 2:
The computing device performs multiple functions: it acts as a central coordinator collecting environmental data from all nodes, runs propagation models for the entire network, analyzes system-wide performance metrics, and distributes optimized parameters back to individual nodes. This multi-functional approach enables automated operation while ensuring system-wide optimization, as the same device that simplifies node configuration also guarantees network-wide performance through centralized coordination
Data Source
AI summary
The present disclosure pertains to a self-configuring network comprising one or more nodes each configured to provide wireless coverage to one or more transceivers. Some embodiments may: obtain a wireless propagation model, including environmental data; determine, via a computing device of the network, a set of optimal operating parameters using the obtained model and the environmental data; and configure, via the computing device, at least one of the one or more nodes with the set of optimal operating parameters. As a result, overall system performance on the larger macro scale may be maximized. That is, improving performance of only one or a few devices, on a micro-scale, may not be sufficient, as this may be achieved at the expense of harming performance of other devices.