Small Cell Deployment Planning via Simulation Analysis
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Solution Overview
Problem
Current small cell deployment methods are unscientific and costly, failing to utilize detailed user data to provide a reliable and efficient plan, often resulting in inadequate network capacity and inefficient resource allocation.
Innovation Solution
A method for determining network congestion periods, receiving user measurement data, performing simulation analysis based on factors like traffic density, user mobility, and RRC connections, to recommend optimal small cell deployment locations, considering financial and network benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional small cell deployment methods are used, then deployment can be completed, but the process is costly and unscientific with inadequate planning
Solution Approach 1:
The system performs simulation analysis and generates deployment plans before actual deployment. It evaluates multiple potential locations using measurement data and performance metrics to predetermined criteria, allowing optimization of deployment strategy in advance rather than reacting during deployment.
Solution Approach 2:
The system continuously receives measurement data from mobile devices and network elements, assesses network performance based on this feedback, and uses the results to refine deployment recommendations. The simulation analysis compares predicted performance against target criteria to validate deployment plans.
2Productivity
If small cells are deployed without detailed user data analysis, then deployment is simpler, but network capacity and efficiency are insufficient
Solution Approach 1:
The system automatically collects measurement data from mobile devices and network elements, processes this data through simulation analysis, and generates deployment recommendations without requiring manual intervention. The system serves itself by using its own collected data to optimize its own deployment decisions.
Solution Approach 2:
The system evaluates multiple deployment scenarios by changing parameters such as base station location, type of small cell, and configuration settings. It analyzes how different parameter combinations affect network performance metrics to identify optimal deployment configurations.
3Measurement precision
If simulation analysis is performed for all possible locations, then deployment optimization is maximized, but processing time and computational resources increase
Solution Approach 1:
The system performs simulation analysis for all possible locations to ensure comprehensive evaluation, accepting the computational cost to achieve maximum deployment optimization. This excessive action ensures no potentially optimal location is missed, with the benefit of scientifically validated deployment decisions.
Data Source
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AI summary
Techniques for providing a small cell deployment plan are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for providing a small cell deployment plan. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to determine a time period in which network congestion is experienced within a mobile communications network. The one or more processors may also be configured to receive measurement data from mobile communications devices of users within the mobile communications network at or around the time period. The one or more processors may further be configured to assess performance of the mobile communications network based upon the measurement data. The one or more processors may also be configured to perform simulation analysis to determine impact of placing an additional base station at one or more locations with the mobile communications network. The one or more processors may additionally be configured to provide a recommendation for deployment of one or more base stations based on the simulation analysis.