Spectrum prediction algorithm in ISM band based on two-dimensional LMBP neural network

Xiaoyu WAN; Pan HU; Zhengqiang WANG

doi:10.11959/j.issn.1000-0801.2016083

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Spectrum prediction algorithm in ISM band based on two-dimensional LMBP neural network

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

- Spectrum prediction algorithm in ISM band based on two-dimensional LMBP neural network
- Telecommunication Science Vol. 32, Issue 3, Pages: 53-59(2016)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2016083
  CLC： TN925
- Published Online：2016-03，
  
  Published：20 March 2016
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Xiaoyu WAN, Pan HU, Zhengqiang WANG. Spectrum prediction algorithm in ISM band based on two-dimensional LMBP neural network[J]. Telecommunication science, 2016, 32(3): 53-59.
DOI：

Xiaoyu WAN, Pan HU, Zhengqiang WANG. Spectrum prediction algorithm in ISM band based on two-dimensional LMBP neural network[J]. Telecommunication science, 2016, 32(3): 53-59. DOI： 10.11959/j.issn.1000-0801.2016083.

摘要

随着短距离无线通信技术的快速发展及应用，ISM（2.4 GHz）频段的电磁干扰问题日益凸现，而利用频谱预测来预先获知频段的占用信息，已成为解决设备间兼容共存问题的有效途径。在验证ISM频段时域频域相关性的基础上，提出了一种时频二维LMBP神经网络，并将其应用于ISM频段的频谱预测。通过仿真和理论分析得到了最佳的时频训练组合点（△t=5、△f=2），在神经网络输入向量N=9的条件下，该点的预测准确度可达95%，相比Markov算法和时域LMBP神经网络分别提高了9%和4%的预测精度，且具有更优的训练收敛时间。

Abstract

With the rapid development and application of short-range wireless communications technology，the electromagnetic interference of ISM（2.4 GHz）band has become more apparent.Using the spectral prediction algorithm to predict the information of spectrum occupancy has become an effective way to solve the problem of compatible coexistence between devices.On the basis of verifying the time-domain and frequency-domain correlation of ISM band，an LMBP neural network of time and frequency domain was proposed and applied in the spectral prediction of ISM band.Through simulations and theoretical analysis，the best training combination of time-frequency point （△t=5，△f=2）was obtained.This point improves 95% of the spectrum prediction accuracy under the conditions of the input vector N=9 of the neural network.It increased 9% and 4% prediction accuracy compared with Markov algorithm and time-domain LMBP neural network and it had a better convergence time of training.

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GUNGOR V C ， HANCKE G P . Industrial wireless sensor networks： challenges，design principles and technical approaches [J ] . IEEE Transactions on Industrial Electronics ， 2009 ， 56 （ 10 ）： 4258 - 4265 .

FIRDAU S ， NUGROHO E ， SAHRONI A . ZigBee and wifi network interface on wireless sensor networks [C ] // 2014 Makassar International Conference on Engineering and Informatics ， November 26 - 30 ， 2014 ， Makassar，Indonesia . New Jersey ： IEEE Press ， 2014 ： 54 - 58 .

XU R T ， SHI G T ， LUO J ， et al . MuZi：multi-channel ZigBee networks for avoiding wifi interference [C ] // 2011 International Conference on Internet of Things and 4th International Conference on Cyber，Physical and Social Computing ， October 19 - 22 ， 2011 ， Dalian，China . New Jersey ： IEEE Press ， 2011 ： 323 - 329 .

MITOLA J ， MAQUIRE G Q . Cognitive radios：making software radios more personal [J ] . IEEE Transactions on Personal Communications ， 1999 ， 6 （ 4 ）： 13 - 18 .

XING X ， JING T ， CHENG W ， et al . Spectrum prediction in cognitive radio networks [J ] . IEEE Transactions on Wireless Communications ， 2013 ， 20 （ 2 ）： 90 - 96 .

HUANG P ， LIU C J ， YANG X ， et al . Wireless spectrum occupancy prediction based on partial periodic pattern mining [J ] . IEEE Transactions on Parallel and Distributed Systems ， 2014 ， 25 （ 7 ）： 1925 - 1934 .

吴建绒，胡津铭，秦继新 . 基于K-RBF 神经网络的认知无线电频谱预测 [J ] . 电视技术， 2014 ， 38 （ 5 ）： 105 - 108 .

WU J R ， HU J M ， QIN J X . Prediction of spectrum based on K-RBF neural network in cognitive radio [J ] . Video Engineering ， 2014 ， 38 （ 5 ）： 105 - 108 .

张松 . 基于马尔科夫链的ZigBee信道选择算法的研究 [D ] . 上海：上海海洋大学， 2014 .

ZHANG S . ZigBee channel selection algorithm research based on Markov chain [D ] . Shanghai ： Shanghai Ocean University ， 2014 .

陈斌华 . 认知无线电系统中的频谱预测算法研究 [D ] . 北京：北京邮电大学， 2011 .

CHEN B H . Research on the spectrum prediction algorithm in cognitive radio system [D ] . Beijing ： Beijing University of Posts and Telecommunications ， 2011 .

CHEN D W ， YIN S X ， QIAN Z ， et al . Mining spectrum usage data：a large-scale spectrum measurement study [J ] . IEEE Transactions on Mobile Computing ， 2012 ， 11 （ 6 ）： 1033 - 1046 .

BAI S ， ZHOU X ， XU F J ， et al . “Soft decision”spectrum prediction based on improved-back-propagation neural networks [C ] // 2015 11th International Conference on Natural Computation ， April 27 - 29 ， 2014 ， Zhangjiajie，China . New Jersey ： IEEE Press ， 2015 ： 128 - 133 .

DING G ， WANG J ， WU Q ， et al . Joint spectral-temporal spectrum prediction from incomplete historical observations [C ] // 2014 IEEE Global Conference on Signal and Information Processing ， October 19 - 22 ， 2011 ， Atlanta，USA . New Jersey ： IEEE Press ， 2014 ： 1325 - 1329 .

DAS D ， DAS S ， et al . A survey on spectrum occupancy measurement for cognitive radio [J ] . Wireless Personal Communications ， 2015 ， 85 （ 4 ）： 2581 - 2598 .

习国泰 . 改进Levenberg-Marquardt算法的复杂度分析 [D ] . 上海：上海交通大学， 2012 .

XI G T . On the complexity of the modified Levenberg-Marquardt algorithm for nonlinear equations [D ] . Shanghai ： Shanghai Jiaotong University ， 2012 .

ZHOU G ， JENNIE S . Advanced neural network training algorithm with reduced complexity based on Jacobian deficiency [J ] . IEEE Transactions on Neural Networks ， 1998 ， 9 （ 3 ）： 445 - 453 .

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