Network Optimization via Exploration Data Feedback
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Solution Overview
Problem
Current network optimization methods are time-consuming and inefficient, especially for ultra-reliable and low-latency communications (URLLC), as they require human intervention and are not effective in addressing dynamic network changes, leading to potential performance degradation due to unknown scenarios.
Innovation Solution
The implementation of exploration procedures in radio networks, where exploration data is generated and transmitted between network elements to simulate and optimize network conditions without affecting live traffic, using reinforcement learning to update optimization algorithms and improve network performance proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional network optimization methods are used, then network performance can be improved, but the process is time-consuming and requires human intervention
Solution Approach 1:
The patent applies preliminary action by generating exploration data and training machine learning models in advance using simulated network conditions before actual deployment. This allows the optimization algorithm to be pre-trained on various network scenarios, reducing the time required for real-time optimization while maintaining high reliability through pre-validated models
Solution Approach 2:
The patent creates copies of network conditions through exploration data that mimics real traffic patterns without using actual user data. This copying approach allows extensive optimization testing and training to be performed on simulated data, eliminating time-consuming human intervention while preserving the effectiveness of performance optimization
2Reliability
If traditional optimization methods are used, then network performance can be improved, but they are not effective in addressing dynamic network changes
Solution Approach 1:
The patent implements dynamics by using machine learning models that continuously learn from exploration data and adapt to changing network conditions. The system dynamically updates optimization parameters based on real-time network state, enabling it to respond effectively to dynamic changes while maintaining performance optimization through adaptive algorithms
Solution Approach 2:
The patent applies feedback mechanisms where exploration data is collected from the network, processed through machine learning models, and used to generate optimization recommendations that are fed back into the network. This closed-loop feedback system enables continuous adaptation to dynamic changes while maintaining optimal performance through iterative learning and adjustment
3Extent of automation
If exploration data is generated and transmitted between network elements, then automated optimization is achieved, but device complexity increases
Solution Approach 1:
The patent applies universality by designing exploration data structures and processing algorithms that can be implemented across multiple network elements with similar functionality. The machine learning models are trained to handle various network scenarios through a unified approach, reducing the complexity burden on individual devices while achieving system-wide automation through standardized multi-functional components
Data Source
AI summary
An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element. In another example implementation, the method may include receiving, by a second network element, exploration data from a first network element; generating, by the second network element, exploration data feedback, the exploration data feedback generated in response to and based on the exploration data received from the first network element; and transmitting, by the second network element, the exploration data feedback to the first network element.