Network Optimization Using Combined UL DL RF Data
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Solution Overview
Problem
Conventional mobile communication system network optimization processes are time-consuming and costly, requiring skilled engineers and focusing only on DownLink (DL) RF status information, which limits optimization to specific test routes and does not account for neighbor eNB performance.
Innovation Solution
An apparatus and method that automatically perform network optimization using both UpLink (UL) and DL RF status information, enabling real-time output and remote adjustment of RF and antenna parameters for entire service areas, including neighbor eNBs, to enhance performance across all areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual network optimization process is performed by skilled engineers using DL RF status information, then network performance can be optimized, but the process is time-consuming and costly
Solution Approach 1:
The system enables self-service optimization by automatically collecting UL RF status information from eNBs and DL RF status information from UEs, analyzing the combined data through propagation models to predict RF environments, and determining optimized parameters without requiring manual engineer intervention. The network optimization function serves itself through automated data collection, analysis, and parameter determination.
Solution Approach 2:
The patent replaces the mechanical manual process of engineer analysis and parameter adjustment with an automated electronic system that collects RF status information, processes data through propagation models, and automatically determines optimized parameters. This substitution of manual mechanical analysis with automated electronic processing significantly reduces optimization time while maintaining reliability.
2Ease of manufacture
If network optimization focuses only on DL RF status information from test routes, then optimization can be performed with available data, but neighbor eNBs and other areas are not optimized
Solution Approach 1:
The system achieves multi-functionality by simultaneously optimizing multiple eNBs and their neighbor cells across different service areas. By collecting UL RF status information from multiple eNBs and DL RF status information from multiple UEs, the system performs comprehensive optimization that extends beyond single-cell or test-route limitations to cover entire service areas including neighbor eNBs.
Solution Approach 2:
The patent adds a new dimension to network optimization by incorporating UL RF status information from eNBs alongside traditional DL RF status information from UEs. This dual-directional approach enables the system to analyze and optimize not only the serving cell but also neighbor cells and broader service areas, expanding the optimization scope from single-point to multi-dimensional coverage.
3Measurement precision
If drive test is performed to collect DL RF status information, then RF environment can be measured, but UL RF status information and neighbor eNB performance cannot be obtained
Solution Approach 1:
The system merges two previously separate information sources: UL RF status information collected from eNBs and DL RF status information collected from UEs during drive tests. By combining these complementary data sources, the system achieves comprehensive RF environment measurement that includes both uplink and downlink characteristics, enabling optimization of serving and neighbor cells simultaneously.
Data Source
AI summary
Disclosed is a method for performing a network optimization process in a mobile communication system. The method includes receiving UpLink Radio Frequency (UL RF) information about a UL signal which a at least one eNB receives from a device in which a drive test is performed, receiving DownLink (DL) RF information about a DL signal which the device receives from the at least one eNB, predicting an RF environment and performance for the at least one eNB based on the received UL RF information and DL RF information, and determining at least one parameter for network optimization which is applied to the network optimization process based on the predicted RF environment and performance for the at least one eNB.