Cell Tower Signal Intelligence

Published: 2025-11-15

Cell Tower Signal Intelligence

A comprehensive telecom intelligence platform that bridges AI assistants with geospatial tower data through the Model Context Protocol (MCP). This full-stack ecosystem enables LLMs to query, analyze, and interact with cellular tower infrastructure data seamlessly.

View GitHub

Technologies Used

TypeScript Java Python Spring Boot React PostgreSQL Google Maps

Project Overview

The Cell Tower Signal Intelligence system consists of three interconnected components:

  • MCP Tool Server - Exposes controlled API access for AI assistants
  • Backend API - Spring Boot REST service with PostgreSQL database
  • Frontend Dashboard - React-based analytics and visualization interface

Architecture

+-----------------------------+
|          Frontend           |
|  React + TS + Google Maps   |
|  AI Insights Dashboard      |
+---------------+-------------+
                |
                | REST API
                v
+-----------------------------+
|           Backend           |
|  Spring Boot API            |
|  PostgreSQL Database        |
|  Advanced Query & Analytics |
+---------------+-------------+
                |
                | MCP Tools
                v
+-----------------------------+
|          MCP Tool           |
|  Exposes API to LLMs        |
|  Tools: get_towers, search… |
+-----------------------------+

Key Features

MCP Tool Server

  • AI-First Design: Purpose-built for LLM integration
  • Controlled Access: Secure API exposure through MCP protocol
  • Rich Toolset: 12+ specialized functions for tower data manipulation
  • Coverage Analysis: Composite statistics and insights generation

Backend API

  • Geospatial Queries: Location-based tower filtering with bounding-box search
  • Signal Analytics: Filter towers by signal strength (dBm) ranges
  • Radio Technology Support: LTE, GSM, UMTS, CDMA compatibility
  • Advanced Pagination: Efficient data retrieval with sorting options
  • Full CRUD Operations: Complete tower lifecycle management

Frontend Dashboard

  • Interactive Mapping: Google Maps integration with real-time tower visualization
  • AI Insights Interface: Seamless integration with MCP-enabled assistants
  • Mobile Responsive: Optimized for all device sizes
  • Data Export Tools: CSV and JSON export capabilities
  • Filter Interface: Advanced tower discovery and analysis tools

Technical Implementation

Core Technologies

  • Backend: Spring Boot, PostgreSQL, Java
  • Frontend: React, TypeScript, Vite, Google Maps API
  • MCP Server: Python with asyncio for concurrent processing
  • Database: PostGIS for geospatial data operations

MCP Tool Functions

The MCP server provides specialized tools for AI assistants:

  • get_all_towers - Retrieve complete tower dataset
  • get_towers_paged - Paginated tower queries with sorting
  • get_tower_by_id - Individual tower lookup
  • get_towers_by_radio - Filter by radio technology
  • get_towers_by_location - Geospatial bounding-box search
  • get_towers_by_signal_range - Signal strength filtering
  • analyze_coverage - Generate coverage statistics and insights

Data Model

Each tower record includes:

  • Location: Latitude/longitude coordinates
  • Network Info: MCC, NET, AREA, CELL identifiers
  • Technology: Radio type (LTE, GSM, UMTS, CDMA)
  • Signal Metrics: Range, sample count, average signal strength

Demonstration

The project includes comprehensive video demonstrations showcasing:

  1. API Overview - System architecture and endpoint walkthrough
  2. Frontend UI - Dashboard features and interactive mapping
  3. Integration Demo - Real-time API and map coordination
  4. Final Showcase - Complete workflow from query to visualization

Development Journey

This project emerged from the need to make telecom infrastructure data accessible to AI assistants. The MCP integration allows LLMs to understand and analyze cellular coverage patterns, identify network gaps, and provide intelligent insights about telecommunication infrastructure.

Key Technical Challenges Solved:

  • Efficient geospatial data querying at scale
  • Real-time map visualization with large datasets
  • Secure AI assistant API access through MCP protocol
  • Cross-platform responsive design implementation

Impact & Applications

  • Network Planning: AI-assisted coverage gap analysis
  • Infrastructure Optimization: Data-driven tower placement recommendations
  • Rural Connectivity: Enhanced service accessibility insights
  • Emergency Response: Rapid coverage assessment capabilities

The system demonstrates how modern AI integration can transform traditional telecom data analysis, making complex geospatial queries accessible through natural language interfaces while maintaining data security and performance.

This project showcases the intersection of telecommunications, geospatial analysis, and AI integration - representing a new paradigm for intelligent infrastructure management.