Small Cell Planning Tool Using Geolocated Traffic Data
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Solution Overview
Problem
Wireless communication service providers face challenges in accurately determining candidate locations for small cells within their networks due to decreasing cell coverage areas, where predictive models and data-gathering techniques are becoming less effective.
Innovation Solution
A small cell planning tool that collects geolocated traffic data and wireless network data to identify high-traffic areas, assesses current network performance, and calculates scores for candidate locations, recommending solutions such as deploying small cells, distributed antenna systems, or cell splits to improve coverage and capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive models and data-gathering techniques are used to determine candidate locations for cells, then network planning can be performed, but the accuracy of these models decreases as cell coverage area decreases
Solution Approach 1:
The patent replaces traditional predictive modeling approaches with a machine learning-based system that uses actual traffic data and network performance measurements. Instead of relying on theoretical models that become inaccurate at small scales, the system learns patterns from real-world data, substituting the mechanical predictive approach with a data-driven adaptive approach that maintains accuracy regardless of cell size.
Solution Approach 2:
The system changes the parameters used for location determination by incorporating multiple data sources including traffic volume, network capacity, coverage quality, and interference levels. By adjusting and weighting these parameters dynamically based on learned patterns, the system adapts to different cell sizes and deployment scenarios, maintaining accuracy whether dealing with large macrocells or small microcells.
2Reliability
If traditional cell deployment methods are used, then network coverage can be provided, but network efficiency and service quality deteriorate in high-traffic areas
Solution Approach 1:
The system performs preliminary analysis of traffic patterns, network capacity, and coverage quality before deploying small cells. By using machine learning to predict high-demand areas and pre-identify optimal locations, the system prepares deployment strategies in advance, ensuring that cells are placed where they will most effectively improve service quality and network efficiency before actual deployment occurs.
Solution Approach 2:
The system implements continuous monitoring of network performance metrics including traffic volume, signal quality, and capacity utilization. This feedback is fed back into the machine learning model to refine location recommendations and optimize existing cell placements, creating a closed-loop system that continuously improves service quality and network efficiency based on actual performance data.
Data Source
AI summary
Systems and methods are described for managing deployment of small cells in a wireless telecommunications network. A wireless telecommunications service provider obtains geolocated traffic data associated with the geographic coverage area of its network. The provider utilizes a planning tool to apply a clustering algorithm to the traffic data and identify areas of high traffic density as candidate locations. The planning tool may evaluate the candidate locations against the existing coverage and capacity of the wireless telecommunications network, and may identify solutions for the particular issues identified at the candidate location. The candidate locations, evaluation scores, and identified solutions may be output for display as a map or table, and the tool may automate various aspects of evaluating, recommending, and implementing identified solutions.