Wireless Access With Multifactor Local TE Authentication
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Solution Overview
Problem
Existing wireless communication systems face challenges in optimizing network performance across diverse environments, including heterogeneous wireless systems with 4G/5G/WiFi networks, stationary and mobile vehicles, and unlicensed band communication devices, without effective methods for managing network resources and predicting future conditions.
Innovation Solution
The implementation of machine learning algorithms to enhance wireless communication protocols by training models on network resource usage, signal quality, and traffic patterns, allowing for predictive analysis and dynamic adjustments of network parameters such as caching resources, Dynamic Spectrum Sharing (DSS), and energy-efficient strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning algorithms are applied to optimize network performance, then Quality of Service (QoS) and throughput are improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously receives performance data from wireless communication systems, processes this feedback to refine its predictions, and dynamically adjusts network parameters. This closed-loop approach enables the system to learn from actual performance and improve QoS over time while managing complexity through automated adaptive control
Solution Approach 2:
The system dynamically adjusts network parameters such as resource allocation, transmission power, and scheduling decisions based on real-time conditions and machine learning predictions. By continuously optimizing parameters rather than using fixed configurations, the system adapts to changing network conditions to maximize throughput and QoS while the ML model manages the complexity of parameter optimization
2Productivity
If dynamic adjustments of network parameters are made in real-time, then throughput is maximized, but computational requirements increase
Solution Approach 1:
The machine learning model performs preliminary actions by predicting future network conditions and optimal parameter settings before actual communication occurs. The model processes historical data and patterns to forecast future states, allowing the system to prepare resource allocations and configurations in advance, thereby maximizing throughput while reducing real-time computational burden
Solution Approach 2:
The patent replaces complex real-time computational optimization with machine learning-based predictive models that have been pre-trained on historical data. Instead of performing computationally intensive optimization calculations during each communication instance, the system uses pre-computed predictions from the ML model, significantly reducing real-time computational requirements while maintaining high throughput
3Measurement precision
If machine learning models are trained on extensive network data, then prediction accuracy improves, but data processing time increases
Solution Approach 1:
The machine learning models are trained in advance on extensive historical network data to achieve high prediction accuracy. This preliminary training process occurs offline, allowing the models to learn complex patterns and relationships from large datasets without impacting real-time operation. The trained models can then make accurate predictions quickly during actual network operation
Solution Approach 2:
The data processing is segmented into two distinct phases: an offline training phase where extensive data is processed to train the models, and an online inference phase where the trained models make predictions in real-time. This segmentation allows extensive data processing to occur without time constraints during operation, while maintaining fast and accurate predictions during actual network use
Data Source
AI summary
A method of facilitating network access for local Terminal Equipment (TE) involves utilizing a Mobile Terminal (MT) that is pre-equipped with a list of authorized TE identities and a user identity module. Upon receiving an identity authentication signal from the TE, the MT assesses the TE's identity against the authorized list through multifactor verification. If the TE is verified as authorized, the MT retrieves a unique identifier from the user identity module and conveys this identifier to the TE. Subsequently, the TE employs the provided identifier to access the network.