A Collaborative Wideband Compressed Spectrum Sensing Scheme Based on Supervised Learning

MA Bin; WANG Hong-ming; XIE Xian-zhong

doi:10.3969/j.issn.0372-2112.2020.12.008

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A Collaborative Wideband Compressed Spectrum Sensing Scheme Based on Supervised Learning

更新时间：2025-12-08

- A Collaborative Wideband Compressed Spectrum Sensing Scheme Based on Supervised Learning
- Acta Electronica Sinica Vol. 48, Issue 12, Pages: 2338-2344(2020)
- 作者机构：
  
  1. 重庆邮电大学移动通信技术重庆市重点实验室,重庆,400065
  2. 重庆邮电大学通信与信息工程学院,重庆,400065
  3. 重庆邮电大学移动通信技术重庆市重点实验室,重庆,400065
  4. 重庆邮电大学通信与信息工程学院,重庆,400065
- 作者简介：
- 基金信息：
  
  Science and Technology Research Major Program of Chongqing Municipal Education Commission (No.KJZD-M201900602);Basic Research and Frontier Exploration Project of Chongqing Municipality (No.CSTC2018jcyjAX0432);Chongqing Postgraduate Research and Innovation Project (No.CYS19252)
- DOI：10.3969/j.issn.0372-2112.2020.12.008
  CLC： TN929.5
- Published Online：25 December 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
MA Bin, WANG Hong-ming, XIE Xian-zhong. A Collaborative Wideband Compressed Spectrum Sensing Scheme Based on Supervised Learning[J]. Acta Electronica Sinica, 2020, 48(12): 2338-2344.
DOI：

MA Bin, WANG Hong-ming, XIE Xian-zhong. A Collaborative Wideband Compressed Spectrum Sensing Scheme Based on Supervised Learning[J]. Acta Electronica Sinica, 2020, 48(12): 2338-2344. DOI： 10.3969/j.issn.0372-2112.2020.12.008.

摘要

宽带压缩频谱检测存在信号稀疏度未知和次用户检测开销过大的问题.因此，本文提出一种高效的协作宽带压缩频谱检测方案.首先，推导了一种基于学习的稀疏度自适应预测模型.其次，设计了一种宽带频谱筛选算法.最后，提出一种协作宽带压缩频谱检测方案.仿真结果表明，自适应预测模型的拟合效果优于现有预测模型，并且所提检测方案也有效地降低了次用户采样率和频谱重构时延.

Abstract

Wideband compressed spectrum sensing has the problems of unknown signal sparsity and high overhead of secondary users sensing. Therefore

this paper proposed an efficient cooperative scheme of wideband compressed spectrum sensing. Firstly

based on learning

a sparsity adaptive learning prediction model was derived. Secondly

a wideband spectrum filtering algorithm is designed. Finally

a cooperative wideband compressed spectrum sensing scheme was proposed. The simulation results show that the fitting effect of the adaptive prediction model are better than the existing prediction model

and the proposed sensing scheme effectively reduces the sampling rate and spectrum reconstruction delay of secondary users.

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