A Sequential Spectrum Sensing Method Using Grouped Data

doi:10.7652/xjtuxb201512006

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A Sequential Spectrum Sensing Method Using Grouped Data

更新时间：2025-07-09

- A Sequential Spectrum Sensing Method Using Grouped Data
- Vol. 49, Issue 12, Pages: 34-39+150(2015)
- 作者机构：
  
  南京工业大学信息与计算科学系,南京,210009
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201512006
  CLC： TN911.23
- Online First：10 December 2015，
  
  Published：2015
- 稿件说明：
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A Sequential Spectrum Sensing Method Using Grouped Data[J]. 2015, 49(12): 34-39+150.
DOI：

A Sequential Spectrum Sensing Method Using Grouped Data[J]. 2015, 49(12): 34-39+150. DOI： 10.7652/xjtuxb201512006.

摘要

针对传统的认知无线电频谱感知方法在低信噪比下感知时间长、系统吞吐量低的问题

提出了一种采用分组数据的混合型序贯检测(MSD)方法。该方法首先将次用户的感知数据进行分组形成超采样

然后由数学理论推导出最大化系统吞吐量的最优虚警概率

并且利用牛顿迭代法搜索最优虚警概率

最后在最优虚警概率下对超采样序列依次进行细检测和粗检测

快速获得检测结果。MSD方法采用分组数据进行频谱感知

能有效缩短感知时长

获得最大系统吞吐量

从而提高频谱利用率。蒙特卡罗仿真结果表明

在低信噪比下MSD方法比传统的序贯检测法和序贯能量检测法的平均归一化吞吐量增加了109%和21%

平均感知开销率减少了75%和49%。

Abstract

A mixed sequential detection(MSD)method using grouped data is proposed to solve the problems of long sensing time and low system throughput of conventional spectrum sensing methods under low signal-noise ratio(SNR)condition. At first

the sensing data of the second user are processed in segments and grouped in super samples. Then

the optimal false alarm probability of the maximum system throughput is derived through mathematical theory analysis

and the Newton iterative algorithm is applied to search the optimal false alarm probability. Finally

the fine and rough detections on a sequence of super samples are successively taken under the optimal false alarm probability to quickly obtain the results of detection. The MSD performs spectrum sensing using grouped data

can effectively reduce the sensing time

and achieve the maximum throughput and improve the spectral efficiency. Monte Carlo simulation results under low SNR and comparisons with the sequential detection and the sequential energy detection show that the MSD gets 109%

21% increase in average normalized throughput and 75%

49% decrease in the ratio of average sensing overhead

respectively.

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references

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