Spectrum sensing algorithm based on average value of middle part of power spectral density

Zhijin ZHAO; Xi LV; Shilian ZHENG

doi:10.11959/j.issn.1000-0801.2016196

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Spectrum sensing algorithm based on average value of middle part of power spectral density

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- Spectrum sensing algorithm based on average value of middle part of power spectral density
- Telecommunications Science Vol. 32, Issue 7, Pages: 53-60(2016)
- 作者机构：
  
  1. 杭州电子科技大学浙江省数据存储传输及应用技术研究重点实验室，浙江杭州310018
  2. 中国电子科技集团第36研究所通信系统信息控制技术国家级重点实验室，浙江嘉兴314001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2016196
  CLC： TN925
- Published Online：2016-07，
  
  Published：15 July 2016
- 稿件说明：
移动端阅览
Zhijin ZHAO, Xi LV, Shilian ZHENG. Spectrum sensing algorithm based on average value of middle part of power spectral density[J]. Telecommunications science, 2016, 32(7): 53-60.
DOI：

Zhijin ZHAO, Xi LV, Shilian ZHENG. Spectrum sensing algorithm based on average value of middle part of power spectral density[J]. Telecommunications science, 2016, 32(7): 53-60. DOI： 10.11959/j.issn.1000-0801.2016196.

摘要

根据有、无主用户信号时接收信号功率谱最大、最小值差值不同的特点，提出了一种基于功率谱密度中段平均的频谱感知算法。针对估计的信号功率谱在最小值附近波动多、最小值难以根据单个点准确给出的问题，利用接收信号功率谱中段平均值估计功率谱的最小值，降低最小值的随机性对频谱感知算法性能的影响。理论推导了检测门限和检测概率的表达式，并对算法进行了仿真分析。仿真结果表明，在AWGN信道和Rayleigh衰落信道中，本文算法性能都优于已有的功率谱密度频谱感知算法。该算法无需主用户信息，不用进行复杂的特征值分解。

Abstract

The difference between maximum and minimum value of the received signal power spectrum is distinct when the primary user signal is present or absent.Using this characteristic

the spectrum sensing algorithm based on the average of the middle part of power spectral density was proposed.Since the minimum of the signal power spectral density fluctuated

the minimum couldn't be accurately estimated from a frequency point.The minimum value of the power spectrum was estimated by using the average value of the middle part of the received signal power spectrum to reduce the effect of the randomness of minimum value on spectrum sensing performance.The expressions of detection threshold and detection probability were derived.Simulation results show that performance of the algorithm is better than those of the present spectrum sensing algorithms based on power spectrum density under the AWGN channel and the Rayleigh fading channel.The algorithm didn't need the primary user information and complicated eigenvalue decomposition.

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references

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