Scholarlink

This paper presents a comparison of a fixed and adaptive threshold-based energy detection technique for Cognitive Radio–Internet of Things (CR-IoT) applications. Simulations were conducted over a wide range of signal-to-noise ratio (SNR) values, from -40 dB to +10 dB, while considering the spectrum utilization occupancy \(\alpha \) of the primary user. The results demonstrate that adaptive thresholding improves detection performance in dynamic noise environments and reduces the overall probability of error across a broad range of \(\alpha \) and very low SNR values. In contrast, while simpler to implement, fixed thresholding struggles to provide accurate detection in a noisy environment. These findings underscore the importance of intelligent threshold selection in designing robust and efficient spectrum sensing strategies for next-generation CR-IoT networks.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Performance-Based Comparison of Energy Detection Using Fixed and Adaptive Thresholds for CR-IoT Systems

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Khadija Lahrouni,
  • Hayat Semlali,
  • Guillaume Andrieux,
  • Jean-François Diouris,
  • Abdelilah Ghammaz

摘要

This paper presents a comparison of a fixed and adaptive threshold-based energy detection technique for Cognitive Radio–Internet of Things (CR-IoT) applications. Simulations were conducted over a wide range of signal-to-noise ratio (SNR) values, from -40 dB to +10 dB, while considering the spectrum utilization occupancy \(\alpha \) of the primary user. The results demonstrate that adaptive thresholding improves detection performance in dynamic noise environments and reduces the overall probability of error across a broad range of \(\alpha \) and very low SNR values. In contrast, while simpler to implement, fixed thresholding struggles to provide accurate detection in a noisy environment. These findings underscore the importance of intelligent threshold selection in designing robust and efficient spectrum sensing strategies for next-generation CR-IoT networks.