Wireless Network Simulation for Connected Vehicle Infrastructure
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Solution Overview
Problem
Current cellular network infrastructure is not designed to handle the high volume of data from highly connected vehicles, and some vehicle tasks require enhanced performance like low latency communication, which existing design methods fail to optimize.
Innovation Solution
A computer simulation method that initializes a simulated environment with parameters like environment size and traffic density, adds simulated wireless nodes with specific parameters, evaluates performance metrics, and adjusts node or environment parameters using machine learning models to achieve optimal network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current cellular network infrastructure is used to accommodate increasing vehicle connectivity, then vehicle communication capability is maintained, but network performance deteriorates due to high volume of data transfer
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict future network performance issues before they occur. The system proactively identifies optimal base station configurations and antenna parameters in advance, allowing the network to be pre-adjusted to handle anticipated high data volumes from connected vehicles, thereby maintaining reliability before degradation occurs.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors network performance metrics and uses this information to adjust base station and antenna parameters in real-time. The machine learning models analyze performance data and feed back optimization recommendations, creating a closed-loop system that maintains network reliability despite increasing data volumes from vehicle connectivity.
2Loss of time
If traditional network design methods are used, then design simplicity is maintained, but optimization for low latency communication fails
Solution Approach 1:
The patent replaces traditional manual network design methods with machine learning-based automated optimization systems. Instead of relying on conventional engineering calculations and manual configuration, the system uses AI algorithms to automatically determine optimal base station and antenna parameters, significantly reducing design time and enabling low-latency optimization that would be infeasible through traditional mechanical design processes.
Solution Approach 2:
The patent applies parameter changes by using machine learning models to dynamically adjust multiple network parameters including base station locations, antenna configurations, beamforming parameters, and power settings. These parameter optimizations are specifically tuned to minimize latency for time-sensitive vehicle communication applications, achieving low-latency performance through systematic parameter tuning rather than traditional fixed design approaches.
3Productivity
If the number of wireless nodes is increased to handle high data volume, then network capacity improves, but system complexity increases
Solution Approach 1:
The patent applies universality by designing base stations and antennas that can dynamically adapt to serve multiple functions and varying traffic conditions. The machine learning-optimized nodes are configured to handle diverse data types and traffic patterns efficiently, allowing the network to achieve high capacity with fewer nodes by maximizing the utilization and versatility of each individual node rather than simply adding more specialized nodes.
Solution Approach 2:
The patent uses copying principles by deploying standardized, pre-optimized base station and antenna configurations that can be replicated across the network. Instead of designing and managing highly complex unique nodes, the system uses machine learning to create optimal template configurations that can be copied and deployed consistently, simplifying system management while maintaining high network capacity through efficient replication of proven designs.
Data Source
AI summary
A method for providing a computer simulation of wireless infrastructure may include initializing a simulated environment. The simulated environment includes a plurality of environment parameters. The method further may include adding a plurality of simulated wireless nodes to the simulated environment to form a simulated wireless network, based on a plurality of node parameters. The method further may include evaluating at least one performance metric of the simulated wireless network. The method further may include adjusting at least one of: the plurality of node parameters and the plurality of environment parameters based at least in part on the at least one performance metric of the simulated wireless network. The method further may include repeating the evaluating and adjusting steps until an optimal solution for the plurality of node parameters is identified based at least in part on the at least one performance metric.