AI-Driven Telecom Overlay Planning for Macro and Small Cells
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Solution Overview
Problem
Current network planning for next-generation telecom networks is time-consuming, costly, and lacks real-time configuration options, scalability, and comprehensive solutions for macro and outdoor small cells, failing to optimize RF parameters and complement existing deployments effectively.
Innovation Solution
A system utilizing AI-driven planning servers that build grids from user equipment data to determine optimal macro and small cell configurations, enabling real-time tweaking of parameters and pipelined implementation for efficient deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional manual network planning is used, then detailed RF parameter optimization can be achieved, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical planning processes with an automated AI-driven system. The planning server uses machine learning models to automatically analyze network data, predict RF parameters, and generate deployment solutions, eliminating the need for manual engineering analysis while maintaining optimization accuracy.
Solution Approach 2:
The system enables self-service planning where the AI model automatically processes network data, performs simulations, and generates deployment recommendations without requiring skilled engineers. The planning server autonomously analyzes spatial measurements, traffic patterns, and network performance to create optimized cell configurations.
2Reliability
If extensive simulations are carried out before commercial deployment, then network optimization is improved, but the cost and time increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-training AI models on historical network data and simulation results before actual deployment. The system uses pre-computed training data to quickly predict network behavior during real deployment scenarios, avoiding the need for time-consuming real-time simulations while maintaining optimization accuracy.
Solution Approach 2:
The system creates virtual copies of network scenarios through AI-driven simulations that replicate real-world conditions. The planning server generates synthetic network data and deployment scenarios that mirror actual deployment conditions, allowing rapid testing and optimization without physical trial deployments.
3Measurement precision
If manual RF planning is performed, then accurate network coverage analysis is achieved, but scalability is limited
Solution Approach 1:
The patent segments the network planning process into modular components: data collection modules, AI prediction modules, simulation modules, and deployment recommendation modules. Each module can independently process different aspects of network planning, enabling the system to scale by adding more modules or processing capacity without compromising the precision of coverage analysis.
Solution Approach 2:
The planning server is designed as a universal system that can handle multiple planning scenarios, network types, and deployment configurations through a single unified AI model. The system adapts to different network conditions and geographical areas automatically, providing scalable solutions that maintain high measurement precision across diverse deployment scenarios.
4Manufacturing precision
If conventional planning approaches are used, then detailed site analysis is possible, but real-time configuration options are lacking
Solution Approach 1:
The patent implements dynamic planning where the AI model continuously processes real-time network data and updates deployment recommendations as conditions change. The system can dynamically adjust cell configurations, antenna orientations, and spectrum allocations in real-time based on current network performance metrics, traffic patterns, and environmental conditions while maintaining detailed site analysis precision.
Data Source
AI summary
The present disclosure provides the planning of the next generation network which complements an existing telecom deployment to achieve the desired KPIs from the combined network for both macro and small cell planning in an existing network in a cost-effective method to deploy ODSC cells and Macro Cells in a heterogeneous network. Planning a next-generation network for an existing telecom operator is a large undertaking, utilizing precious man-hours and most importantly, weeks of work. Using an automated planning methodology to plan macro and small cells for a region like a city, state or even country, planners can deploy solutions to make the most optimum use of existing infrastructure. The invention proposes an automated approach to plan the preferred overlay telecom solutions in an area A being part of an existing telecom deployment.