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Automated Spectrum Sensing and Analysis Framework

Rahil Gandotra, Ruoyu Sun, Mark Poletti, Jiayu Mao, Hao Guo

TL;DR

The paper addresses the challenge of real-time, large-scale spectrum analysis by introducing an end-to-end automated framework deployed across multiple USA locations. It combines remote signal detection, centralized analysis using MATLAB via Python, and web-based visualization to produce actionable metrics like channel occupancy and airtime utilization, stored in a database and backed by long-term NAS storage. Key contributions include a multi-site data collection and transfer pipeline with lossless compression, database-backed analytics, and an operations system for fault-tolerant maintenance. The findings reveal spatial and temporal patterns in spectrum usage, including underutilized bands suitable for spectrum sharing, and demonstrate the framework’s scalability and potential to inform policy and dynamic spectrum sharing strategies.

Abstract

Spectrum sensing and analysis is crucial for a variety of reasons, including regulatory compliance, interference detection and mitigation, and spectrum resource planning and optimization. Effective, real-time spectrum analysis remains a challenge, stemming from the need to analyse an increasingly complex and dynamic environment with limited resources. The vast amount of data generated from sensing the spectrum at multiple sites requires sophisticated data analysis and processing techniques, which can be technically demanding and expensive. This paper presents a novel, holistic framework developed and deployed at multiple locations across the USA for spectrum analysis and describes the different parts of the end-to-end pipeline. The details of each of the modules of the pipeline, data collection and pre-processing at remote locations, transfer to a centralized location, post-processing analysis, visualization, and long-term storage, are reported. The motivation behind this work is to develop a robust spectrum analysis framework that can help gain greater insights into the spectrum usage across the country and augment additional use cases such as dynamic spectrum sharing.

Automated Spectrum Sensing and Analysis Framework

TL;DR

The paper addresses the challenge of real-time, large-scale spectrum analysis by introducing an end-to-end automated framework deployed across multiple USA locations. It combines remote signal detection, centralized analysis using MATLAB via Python, and web-based visualization to produce actionable metrics like channel occupancy and airtime utilization, stored in a database and backed by long-term NAS storage. Key contributions include a multi-site data collection and transfer pipeline with lossless compression, database-backed analytics, and an operations system for fault-tolerant maintenance. The findings reveal spatial and temporal patterns in spectrum usage, including underutilized bands suitable for spectrum sharing, and demonstrate the framework’s scalability and potential to inform policy and dynamic spectrum sharing strategies.

Abstract

Spectrum sensing and analysis is crucial for a variety of reasons, including regulatory compliance, interference detection and mitigation, and spectrum resource planning and optimization. Effective, real-time spectrum analysis remains a challenge, stemming from the need to analyse an increasingly complex and dynamic environment with limited resources. The vast amount of data generated from sensing the spectrum at multiple sites requires sophisticated data analysis and processing techniques, which can be technically demanding and expensive. This paper presents a novel, holistic framework developed and deployed at multiple locations across the USA for spectrum analysis and describes the different parts of the end-to-end pipeline. The details of each of the modules of the pipeline, data collection and pre-processing at remote locations, transfer to a centralized location, post-processing analysis, visualization, and long-term storage, are reported. The motivation behind this work is to develop a robust spectrum analysis framework that can help gain greater insights into the spectrum usage across the country and augment additional use cases such as dynamic spectrum sharing.
Paper Structure (11 sections, 5 figures)

This paper contains 11 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Automated spectrum analysis framework
  3. Signal Detection and Recording in Remote Locations
  4. Data Transfer to Central Location
  5. Data Analysis
  6. Data Visualization
  7. Data Storage
  8. System Management and Maintenance
  9. Results Analysis
  10. Related Work
  11. Conclusion

Figures (5)

  • Figure 1: Spectrum analysis framework
  • Figure 2: Automated spectrum utilization analysis across multiple locations
  • Figure 3: Landing page of the dashboard after a user logs in
  • Figure 4: 3D raw data
  • Figure 5: Example airtime utilization results collected in June 2025 over 450-MHz in mid-band at (a) Anchorage, AK; (b) Louisville, KY; (c) Louisville, CO