Cooperation Spectrum Sensing Detecting Algorithm Based on Featured Belief Points in Cognitive Radio Network

Hongyan Zheng; Bo Wu; Yanpeng Feng; Xianjun Meng

doi:10.3969/j.issn.1000-0801.2013.02.015

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Cooperation Spectrum Sensing Detecting Algorithm Based on Featured Belief Points in Cognitive Radio Network

research and development | 更新时间：2024-06-05

- Cooperation Spectrum Sensing Detecting Algorithm Based on Featured Belief Points in Cognitive Radio Network
- Telecommunications Science Vol. 29, Issue 2, Pages: 89-94(2013)
- 作者机构：
  
  1. 深圳职业技术学院深圳518055
  2. 中南大学信息科学与工程学院长沙410083
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1000-0801.2013.02.015
  CLC：
- Published Online：2013-02，
  
  Published：15 February 2013
- 稿件说明：
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Hongyan Zheng, Bo Wu, Yanpeng Feng, et al. Cooperation Spectrum Sensing Detecting Algorithm Based on Featured Belief Points in Cognitive Radio Network[J]. Telecommunications science, 2013, 29(2): 89-94.
DOI：

Hongyan Zheng, Bo Wu, Yanpeng Feng, et al. Cooperation Spectrum Sensing Detecting Algorithm Based on Featured Belief Points in Cognitive Radio Network[J]. Telecommunications science, 2013, 29(2): 89-94. DOI： 10.3969/j.issn.1000-0801.2013.02.015.

摘要

针对认知无线网络（CRN）中频谱检测准确性与检测效率难以平衡的问题，本文提出一种特征信念的认知无线网络ED/FD协作频谱检测算法。通过单认知用户能量检测与特征信号检测协作模式代替多认知用户协作检测模式，降低通信开销，利用部分可观察马尔可夫决策过程（POMDP）对CRN 建模，将检测准确性与检测效率平衡优化问题转化为POMDP最优值函数求解过程，并采用特征信念控制信念状态规模和在线最大报酬值迭代法求解法逼近最优值，降低算法复杂度。实验结果表明，本文算法能有效取得频谱检测准确性与检测效率之间的平衡，达到在不干扰授权用户的同时提高检测效率的目的。

Abstract

In order to solve the dilemma of the tradeoff between spectrum sensing performance and spectrum sensing efficiency in cognitive radio network，a nove1 ED/FD cooperation spectrum sensing algorithm based on featured belief points was proposed. Firstly，this algorithm desployed the single cognitive user energy detection and feature detection co11aborative detection mode instead of multiple cognitive user cooperative detection，reducing communication overhead. Secondly，it modeled cognitive radio network under dynamic uncertainty using partia11y observable Markov decision processes（POMDP），and transformed the optimization of the tradeoff between sensing performance and sensing efficiency into yielding the optima1 value function of POMDP. Fina11y，a nove1 approach using characteristics of belief to contro1 the scale of belief states was presented，which exploited the maximum online reward value iteration algorithm to approximate the optima1 value. The numerica1 results show that the proposed algorithm is able to meet the requirement of high tracking performance with constraint of 1ow probability of interfering primary users.

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references

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