Congestion Management System for Small Cell Deployment
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Solution Overview
Problem
Telecommunications networks face challenges in identifying and addressing data congestion, particularly in small cell networks, where characterizing and measuring congestion in small geographical areas is complex due to factors like heavy-data users, limited bandwidth, and interference, leading to inefficient deployment of small cells and high deployment costs.
Innovation Solution
A congestion management system that automatically detects and identifies areas of congestion by dividing geographic areas into tiles, sorting and grouping data samples based on congestion characteristics, computing a congestion index, and identifying high-congestion areas for optimal small cell deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If small cell networks are deployed to increase network capacity and mitigate congestion, then network throughput and capacity are improved, but deployment costs and time increase significantly due to site acquisition complexity and backhaul establishment requirements
Solution Approach 1:
The system performs preliminary identification and characterization of congestion areas using existing network data before deployment decisions are made. By pre-processing traffic data, call detail records, and geographic information to create congestion indexes and identify optimal small cell locations, the system prepares deployment targets in advance, reducing the time required for actual deployment while ensuring capacity improvements are placed where most needed
2Measurement precision
If congestion management is performed manually without automated systems, then deployment costs are reduced, but measurement precision and ability to identify congestion areas deteriorate
Solution Approach 1:
The system enables self-service congestion analysis by automatically collecting, processing, and analyzing network traffic data, call detail records, and geographic information without requiring manual intervention. The automated generation of congestion indexes and identification of optimal deployment locations provides precise measurements while reducing the need for complex manual analysis systems
Solution Approach 2:
The system replaces manual congestion analysis and site selection processes with automated computational methods. By substituting human analysts and manual procedures with algorithmic processing of network data and geographic information, the system achieves higher measurement precision while managing complexity through standardized automated workflows
3Productivity
If site acquisition process is simplified to reduce deployment time, then deployment speed is improved, but reliability of site selection and optimal placement deteriorates
Solution Approach 1:
The system performs preliminary identification and evaluation of potential small cell sites using automated analysis of congestion data, traffic patterns, and geographic characteristics before the actual site acquisition process. This pre-screening ensures that only the most suitable locations are pursued, maintaining high reliability of site selection while enabling faster deployment by having candidates ready in advance
Solution Approach 2:
The site selection process is segmented into distinct automated stages: congestion area identification, potential site candidate generation, evaluation against multiple criteria, and ranking. This segmentation allows each stage to be optimized independently, maintaining reliability through systematic evaluation while improving overall deployment speed through parallel processing and automated decision-making
Data Source
AI summary
Systems and methods are described herein for identifying areas of maximum congestion so that small cells can be deployed at those specific sites to obviate network congestion problems. The systems and methods identify several input sources (e.g., call data records, passively collected data using Mobile app, etc.) that can be used to identify data congestion. The systems and methods divide a particular geographic area into smaller tiles of a certain shape (e.g., hexagon, square, etc.). For each of the geographic tiles, the systems and methods sort and group the input data samples based on their associated congestion discriminating characteristics (e.g., timestamps, type of input data, etc.). The systems and methods then compute a congestion index for each geographic tile area based on the grouped data samples. In this manner, the systems and methods identify geographic tile areas and time periods of high congestion.