双向中继系统中基于压缩感知信道估计算法
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摘要
针对提高系统传输的频谱利用率问题,采用将压缩感知理论应用于双向中继系统进行信道估计。详细研究在双向中继系统中信道估计算法的设计方法、实现过程,并对广义正交匹配追踪算法(GOMP)与OMP算法及传统的LS信道估计算法进行分析与比较。结果表明GOMP信道估计算法在均方误差(MSE)和误比特率(BER)及需要运行的时间上均较小。在稀疏的信道中,GOMP算法相比于传统LS信道估计算法能有效地提高频谱利用率,相比于OMP算法提高了实时性,降低系统的复杂度。
In order to improve the spectrum efficiency of the system,the compressed sensing theory is applied to the two-way relay system to estimate the channel. The design method and implementation process of the algorithm are studied on in detail,and the GOMP algorithm is compared with the OMP algorithm and the traditional LS channel estimation algorithm. The simulation results confirm that GOMP channel estimation algorithm has a small mean square error( MSE) performance and bit error rate( BER) and time required to run. For sparse channel,the GOMP algorithm can improve the spectrum utilization efficiency compared with the traditional LS channel estimation algorithm,and improve the realtime performance and reduce the complexity of the system compared with the OMP algorithm.
In order to improve the spectrum efficiency of the system,the compressed sensing theory is applied to the two-way relay system to estimate the channel. The design method and implementation process of the algorithm are studied on in detail,and the GOMP algorithm is compared with the OMP algorithm and the traditional LS channel estimation algorithm. The simulation results confirm that GOMP channel estimation algorithm has a small mean square error( MSE) performance and bit error rate( BER) and time required to run. For sparse channel,the GOMP algorithm can improve the spectrum utilization efficiency compared with the traditional LS channel estimation algorithm,and improve the realtime performance and reduce the complexity of the system compared with the OMP algorithm.
引文
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[9]范馨月,苏艳涛,周非.一种低复杂度稀疏信道估计算法[J].计算机工程,2016,42(11):120-130.
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[11]杨海蓉,张成,丁大为,等.压缩传感理论与重构算法[J].电子学报,2011,39(1):142-148.
[12]何为.压缩感知理论及重构算法性能分析[J].信息与技术,2016,3(45):178-182.
[13]Wang Jian,Seokbeop Kwon,Byonghyo Shim.Generalized Orthogonal Matching Pursuit[J].IEEE Transactions on Signal Processing,2012,60(12):6202-6216.
[14]陈艳杰,王丹.协作通信系统信道估计算法研究[J].计算机仿真,2015,32(8):217-227.
[2]Peng Cheng,Lin Gui,Yun Rui,et al.Compressed Sensing Based Channel Estimation for Two-Way Relay Networks[J].2012 IEEE Wireless Communications Letters,IEEE,2012,1(3):201-204.
[3]叶新荣.基于压缩感知重构算法的MIMO-OFDM稀疏信道估计方法研究[D].南京:南京邮电大学,2014.
[4]何雪云,宋荣方,周克琴.基于压缩感知的OFDM系统稀疏信道估计新方法研究[J].南京邮电大学学报,2010,30(2):60-65.
[5]Yashesh Gaur,Vijay Kumar Chakka.Performance comparison of OMP and Co Sa MP based channel estimation in AF-TWRN scenario[C]∥2012 IEEE International Conference on Computer and Communication Technology,2012:186-190.
[6]Wang Ni-na,Su Yong-tao.Sparse Channel Estimation for OFDM Based Two-Way Relay Networks[C]∥2014 IEEE International Conferenceon Communications(ICC),IEEE,2014:4524-4529.
[7]Zhai Jing,Xu Wen-bo,Niu Kai,et al.Sub-Sampling Quantize-andForward Schemes for Relay Networks[C].Vehicular Technology Conference,2014 IEEE 2014:1-5.
[8]赵龙慧,潘乐炳,李宝清.OFDM稀疏信道估计中改进的OMP算法[J].计算机工程与设计,2015,36(7):1701-1705.
[9]范馨月,苏艳涛,周非.一种低复杂度稀疏信道估计算法[J].计算机工程,2016,42(11):120-130.
[10]杨真真,杨震,孙林慧.信号压缩重构的正交匹配追踪类算法综述[J].信号处理,2013,29(4):486-496.
[11]杨海蓉,张成,丁大为,等.压缩传感理论与重构算法[J].电子学报,2011,39(1):142-148.
[12]何为.压缩感知理论及重构算法性能分析[J].信息与技术,2016,3(45):178-182.
[13]Wang Jian,Seokbeop Kwon,Byonghyo Shim.Generalized Orthogonal Matching Pursuit[J].IEEE Transactions on Signal Processing,2012,60(12):6202-6216.
[14]陈艳杰,王丹.协作通信系统信道估计算法研究[J].计算机仿真,2015,32(8):217-227.