# IRS Assisted Decentralized Learning for Wideband Spectrum Sensing

arXiv: https://arxiv.org/abs/2504.01344 · HTML: https://mlsanity.com/paper/2504.01344/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [LLM Assisted Alpha Fairness for 6 GHz WiFi and NR_U Coexistence: An Agentic Orchestrator for Throughput, Energy, and SLA](https://mlsanity.com/paper/2510.17814/) (2025)

## Abstract (arXiv)

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.