Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

IRS Assisted Decentralized Learning for Wideband Spectrum Sensing

Sicheng Liu, Qun Wang, Zhuwei Qin, Weishan Zhang, Jingyi Wang, Xiang Ma

TL;DR

Dynamic spectrum sharing in industrial environments causes interference and unreliable sensing, motivating a push toward IRS-enhanced spectrum monitoring. The authors integrate Intelligent Reflecting Surfaces with decentralized deep learning to perform wideband spectrum sensing under partial observations, leveraging a multi-task DNN with shared shallow layers and band-specific deep layers plus hierarchical parameter sharing and a cosine-annealing training schedule. Key contributions include an IRS-enhanced channel model with $M$ elements, a scalable DNN architecture that avoids exponential multi-band classifiers, and a distributed learning workflow that decouples learning across bands while sharing global features. Simulation results show improved sensing accuracy and faster convergence under various $SNR$ conditions, outperforming centralized and federated baselines, with robust performance in low-SNR regimes, indicating practical impact for next-generation spectrum management.

Abstract

The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often lead to significant interference and critical failures, creating a trade-off between spectrum scarcity and under-utilization. This paper addresses these challenges by proposing a novel Intelligent Reflecting Surface (IRS)-assisted spectrum sensing framework integrated with decentralized deep learning. The proposed model overcomes partial observation constraints and minimizes communication overhead while leveraging IRS technology to enhance spectrum sensing accuracy. Through comprehensive simulations, the framework demonstrates its ability to monitor wideband spectrum occupancy effectively, even under challenging signal-to-noise ratio (SNR) conditions. This approach offers a scalable and robust solution for spectrum management in next-generation wireless networks.

IRS Assisted Decentralized Learning for Wideband Spectrum Sensing

TL;DR

Dynamic spectrum sharing in industrial environments causes interference and unreliable sensing, motivating a push toward IRS-enhanced spectrum monitoring. The authors integrate Intelligent Reflecting Surfaces with decentralized deep learning to perform wideband spectrum sensing under partial observations, leveraging a multi-task DNN with shared shallow layers and band-specific deep layers plus hierarchical parameter sharing and a cosine-annealing training schedule. Key contributions include an IRS-enhanced channel model with elements, a scalable DNN architecture that avoids exponential multi-band classifiers, and a distributed learning workflow that decouples learning across bands while sharing global features. Simulation results show improved sensing accuracy and faster convergence under various conditions, outperforming centralized and federated baselines, with robust performance in low-SNR regimes, indicating practical impact for next-generation spectrum management.

Abstract

The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often lead to significant interference and critical failures, creating a trade-off between spectrum scarcity and under-utilization. This paper addresses these challenges by proposing a novel Intelligent Reflecting Surface (IRS)-assisted spectrum sensing framework integrated with decentralized deep learning. The proposed model overcomes partial observation constraints and minimizes communication overhead while leveraging IRS technology to enhance spectrum sensing accuracy. Through comprehensive simulations, the framework demonstrates its ability to monitor wideband spectrum occupancy effectively, even under challenging signal-to-noise ratio (SNR) conditions. This approach offers a scalable and robust solution for spectrum management in next-generation wireless networks.

Paper Structure

This paper contains 13 sections, 16 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Spectrum Sharing Model
  4. IRS Enhanced Channel Model
  5. Problem formulation
  6. Distributed Learning-based Spectrum Sensing Design
  7. Deep Neural Network Architecture
  8. Collaborative Learning Framework
  9. Local Training
  10. Parameter Sharing and Averaging
  11. Workflow of Collaborative Learning
  12. Simulation Results
  13. Conclusion

Figures (7)

  • Figure 1: Decentralized Collaborative Spectrum Monitoring with Intelligent Reflecting Surface Enhancement
  • Figure 2: Decentralized Collaborative Neural Network Architecture.
  • Figure 3: Channel gain with IRS
  • Figure 4: Standalone-based methods performance.
  • Figure 5: Decopuled method performance
  • ...and 2 more figures