Asynchronous perception algorithm based on energy detection

Pan YU; Bin LI; Cheng-lin ZHAO

doi:10.11959/j.issn.1000-436x.2017039

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Asynchronous perception algorithm based on energy detection

Correspondences | 更新时间：2024-06-05

- Asynchronous perception algorithm based on energy detection
- Journal on Communications Vol. 38, Issue 3, Pages: 165-173(2017)
- 作者机构：
  
  北京邮电大学信息与通信工程学院，北京 100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61379016)
- DOI：10.11959/j.issn.1000-436x.2017039
  CLC： TN911.7
- Online First：2017-03，
  
  Published：15 March 2017
- 稿件说明：
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Pan YU, Bin LI, Cheng-lin ZHAO. Asynchronous perception algorithm based on energy detection[J]. Journal on Communications, 2017, 38(3): 165-173.
DOI：

Pan YU, Bin LI, Cheng-lin ZHAO. Asynchronous perception algorithm based on energy detection[J]. Journal on Communications, 2017, 38(3): 165-173. DOI： 10.11959/j.issn.1000-436x.2017039.

摘要

在未来异构无线网络中，授权用户（PU）与认知用户（SU）间无法进行协作定时，导致授权用户发射机和认知用户接收机之间存在感知时间差。针对这一异步感知场景，基于贝叶斯统计估计理论提出一种全新的异步感知算法。首先，提出一种统一的动态状态空间模型，来描述可观测能量与动态授权用户状态以及未知时间差之间的关系；然后，利用随机有限集并基于最大后验概率准则设计一种迭代式估计方案；最后，通过粒子滤波以数值逼近方式得到估计结果。仿真结果表明，通过准确获取出感知时间差，所提出的异步感知算法可有效消除接收信号的信息不确定性，从而显著提高频谱感知性能。

Abstract

In the future heterogeneous wireless networks

since primary user (PU) and cognitive secondary user (SU) are not coordinated to be synchronous

it will result in sense timing difference between PU’s transmitter and SU’s receiver.For this asynchronous sense case

a new asynchronous sensing algorithm based on Bayesian estimation theory was proposed.A unified dynamic state space model was first proposed to describe the observable energy relationship with dynamic PU state and unknown timing difference.Then

an iterative estimation scheme was designed using stochastic finite set and the rules of maximum posterior probability.Finally

approximated estimation results were obtained by using a particle filter.The simulation results show that the proposed asynchronous scheme significantly eliminates the uncertainty of the received signal information and thus improves the spectrum sensing performance by obtaining the time difference accurately.

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references

HAYKIN S . Cognitive radio:brain-empowered wireless communications [J ] . IEEE Journal on Selected Area in Communication , 2005 , 23 ( 2 ): 201 - 220 .

AXELL E , LEUS G , LARSSON E G , et al . Spectrum sensing for cognitive radio：state-of-the-art and recent advance [J ] . IEEE Signal Processing Magazine , 2012 , 29 ( 3 ): 101 - 106 .

BOGALE T E , VANDENDORPE L , LONG L B . Sensing throughput tradeoff for cognitive radio networks with noise variance uncertainty [C ] // The 9th International Conference on Cognitive Radio Oriented Wireless Networks . 2014 : 435 - 441 .

OSTOVAR A , ZHENG C . Optimization of cooperative spectrum sensing via optimal power allocation in cognitive radio networks [J ] .I IEEE Transactions on Vehicular Technology , 2016 .

JIAO Y , JOE I . Markov model-based energy efficiency spectrum sensing in cognitive radio sensor networks [J ] . Journal of Computer Networks & Communications , 2016 : 1 - 8 .

LIAO Y , SONG L , HAN Z . Listen and talk:full-duplex cognitive radio networks [M ] . Springer International Publishing , 2016 .

LI B , LI S H , NALLANATHAN A , et al . Deep sensing for next-generation dynamic spectrum sharing:more than detecting the occupancy state of primary spectrum [J ] . IEEE Transactions on Communications , 2015 , 63 ( 7 ): 2442 - 2457 .

VUJITIC B , CACKOV N , VUJITIC S . Modeling and characterization of traffic in public safety wireless network [C ] // International Symposium on Performance Evaluation of Computer & Telecommunication Systems . 2005 : 213 - 223 .

DARRAT A F , ZHONG M . On testing the random walk hypothesis:a model-comparison approach [J ] . Financial Review , 2000 , 35 ( 3 ): 105 - 124 .

PROAKIS J G . Digital communications(5th ed) [M ] . New York : McGraw-HillPress , 2008 .

LI B , LI S H , NALLANATHAN A , et al . Deep sensing for future spectrum and location awareness 5G communications [J ] . IEEE Journal on Selected Areas in Communications , 2015 , 33 ( 7 ): 1331 - 1344 .

RISTIC B , VO B T , FARINA A . A tutorial on Bernoulli filters:theory,implementation and applications [J ] . IEEE Transactions on Signal Processing , 2013 , 61 ( 31 ): 3406 - 3430 .

ARULAMPALAM M S , MASKELL S , GORDON N , et al . A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J ] . IEEE Transactions on Signal Processing , 2002 , 50 ( 2 ): 174 - 188 .

JAZWINSKI A H . Stochastic processes and filtering theory [M ] . New York : AcademicPress , 1970 .

MAHLER R . Statistical multisource-multitarget information fusion [M ] . MA : Artech HousePress , 2007 .

DIGHAM F F , ALOUINI M S , SIMON M K . On the energy detection of unknown signals over fading channels [J ] . IEEE Transactions on Communications , 2007 , 5 ( 1 ): 21 - 24 .

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