Cellular Network Optimization via Distributed Gradient Analysis
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Solution Overview
Problem
Cellular network designs based on static nominal environments fail to optimize performance during short-term and long-term environmental changes, such as traffic fluctuations or weather conditions, due to the time-intensive and costly nature of traditional cell planning processes.
Innovation Solution
A network-optimization controller utilizing machine learning to analyze gradient-report messages from user equipment and base stations to determine optimized network-configuration parameters, dynamically adjusting settings to improve performance and account for environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional cell planning is performed to design cellular network, then initial network coverage and quality of service are achieved, but the network cannot adapt to environmental changes such as traffic fluctuations, construction, or weather conditions
Solution Approach 1:
The patent transforms the static cell planning process into a dynamic system by implementing continuous gradient evaluation and machine learning-based optimization. The network configuration parameters are dynamically adjusted based on real-time environmental changes, traffic patterns, and performance metrics, allowing the system to adapt automatically without requiring periodic manual re-planning
Solution Approach 2:
The system enables self-service optimization through autonomous gradient evaluation by base stations and user equipment, combined with machine learning models that automatically determine optimized configuration parameters. The network optimizes itself continuously without external intervention, adapting to environmental changes in real-time
2Reliability
If cell planning is performed frequently to update network configuration, then network performance adapts to environmental changes, but the process becomes time intensive and costly
Solution Approach 1:
The patent implements continuous feedback loops where base stations and user equipment evaluate gradients of performance metrics with respect to network configuration parameters. This feedback is fed into machine learning models that automatically adjust configurations, enabling continuous optimization without the need for time-intensive periodic cell planning processes
Solution Approach 2:
The patent replaces the mechanical, time-intensive cell planning process with a computational system using gradient evaluation and machine learning. Instead of manual analysis and simulation, the system uses automated algorithms to continuously optimize network parameters, dramatically improving efficiency while maintaining or enhancing optimization quality
3Reliability
If machine learning is used to evaluate gradients from multiple entities, then optimized parameters improve group performance, but the complexity of gradient analysis increases
Solution Approach 1:
The patent segments the gradient evaluation process by having each base station and user equipment independently evaluate local gradients of performance metrics. These distributed gradient evaluations are then aggregated by the machine learning system to determine global optimized parameters, breaking down the complex problem into manageable local components
Solution Approach 2:
The patent introduces a machine learning system as an intermediary that receives gradient information from multiple base stations and user equipment, processes this information, and determines optimized network configuration parameters. This intermediary manages the complexity of analyzing gradients from numerous entities by providing a centralized processing layer
Data Source
AI summary
This document describes techniques and apparatuses for optimizing a cellular network using machine learning. A network-optimization controller determines a performance metric to optimize for a cellular network. The network-optimization controller determines at least one network-configuration parameter that affects the performance metric. The network-optimization controller sends a gradient-request message to multiple base stations that directs multiple wireless transceivers to respectively evaluate gradients of the performance metric relative to the at least one network-configuration parameter. The network-optimization controller receives, from the multiple base stations, gradient-report messages generated by the multiple wireless transceivers, the gradient-report messages respectively including the gradients. The network-optimization controller analyzes the gradients using machine learning to determine at least one optimized network-configuration parameter. The network-optimization controller sends an optimization message to at least one of the multiple base stations that directs at least one of the multiple wireless transceivers to use the at least one optimized network-configuration parameter.