OFDM水声通信系统动态OMP信道跟踪算法
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摘要
针对正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)水声通信系统中最小二乘(Least Square,LS)信道估计算法和静态压缩感知信道估计算法分别存在估计精度低、导频开销大和计算复杂度高、实时性差的缺点,利用水声信道冲激响应的时域相关性,通过建立动态稀疏观测模型,提出一种动态正交匹配追踪(Dynamic Orthogonal MatchingPursuit,D-OMP)信道跟踪算法。该算法仅在初始时刻进行一次完整的正交匹配追踪(OrthogonalMatching Pursuit, OMP)信道估计获取信道支撑集,之后通过连续跟踪前一时刻信道支撑集的变化来跟踪信道。仿真结果表明,在导频开销相同的情况下,与传统LS算法、经典OMP算法相比,所提算法具有更好的信道跟踪性能和较低的算法复杂度。
For the OFDM underwater acoustic communication systems, the least square(LS) channel estimation algorithm has the disadvantages of low estimation accuracy and high pilot overhead, while the static compressive sensing channel estimation algorithm has the disadvantages of high computational complexity and poor real-time performance.Aiming at these problems, a new algorithm called dynamic orthogonal matching pursuit(D-OMP) is proposed by establishing a dynamic sparse observation model based on the temporal correlation of the underwater acoustic channel impulse response. The algorithm only performs a complete OMP channel estimation at the initial time to obtain the channel support set, and then tracks the channel by continuously tracking changes in the previous channel support set.The simulation results show that the proposed algorithm has better channel tracking performance and lower algorithm complexity compared with the traditional LS algorithm and the classical OMP algorithm under the same pilot overhead.
For the OFDM underwater acoustic communication systems, the least square(LS) channel estimation algorithm has the disadvantages of low estimation accuracy and high pilot overhead, while the static compressive sensing channel estimation algorithm has the disadvantages of high computational complexity and poor real-time performance.Aiming at these problems, a new algorithm called dynamic orthogonal matching pursuit(D-OMP) is proposed by establishing a dynamic sparse observation model based on the temporal correlation of the underwater acoustic channel impulse response. The algorithm only performs a complete OMP channel estimation at the initial time to obtain the channel support set, and then tracks the channel by continuously tracking changes in the previous channel support set.The simulation results show that the proposed algorithm has better channel tracking performance and lower algorithm complexity compared with the traditional LS algorithm and the classical OMP algorithm under the same pilot overhead.
引文
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[3]GUO S C,HE Z Q,JIANG W P,et al.Channel estimation based on compressed sensing in high-speed underwater acoustic communication[C]//IEEE 9th International Conference on Information,Communications and Signal Processing,2013:1-5.
[4]BERGER C R,ZHOU S L,PREISIG J C,et al.Sparse channel estimation for multicarrier underwater acoustic communication:From subspace methods to compressed sensing[J].IEEE Transactions on Signal Processing,2010,58(3):1708-1721.
[5]WANG D H,NIU K,BIE Z S,et al.A new channel estimation method based on distributed compressed sensing[C]//IEEE Wireless Communications and Networking Conference,2010:1-4.
[6]GONG B,QIN Q,REN X,et al.Distributed compressive sensing based doubly selective channel estimation for large-scale MIMOsystems[J].Mathematics,2015,arXiv:1511.02592v1[cs.IT].
[7]GONG B,LIN G,QIN Q,et al.Block distributed compressive sensing based doubly selective channel estimation and pilot design for large-scale MIMO systems[J].IEEE Transactions on Vehicular Technology,2017,66(10):9149-9161.
[8]周跃海,曹秀岭,陈东升,等.长时延扩展水声信道的联合稀疏恢复估计[J].通信学报,2016,37(2):165-172.ZHOU Yuehai,CAO Xiuling,CHEN Dongsheng,et al.Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread[J].Journal on Communications,2016,37(2):165-172.
[9]VASWANI N.Kalman filtered compressed sensing[C]//IEEE 15th International Conference on Image Processing,2008:893-896.
[10]WANG D H,NIU K,HE Z Q,et al.Pilot-aided channel estimation method based on compressed sensing and Kalman filtering in OFDM systems[C]//IEEE International Conference on Wireless Information Technology and Systems,2010:1-4.
[11]DING X,CHEN W,WASSELL I.Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals[C]//IEEE International Conference on Communications,2015:6675-6680.
[12]GAO Y,XU K,CHEN Y.A novel method of multi-band spectrum sensing exploiting dynamic compressive sensing[C]//IEEE 13th International Conference on Signal Processing,2016:1152-1156.
[13]叶新荣,朱卫平,孟庆民.基于SAMP重构算法的OFDM系统稀疏信道估计方法[J].信号处理,2012,28(3):392-396.YE Xinrong,ZHU Weiping,MENG Qingmin.SAMP construction based sparse channel estimation for OFDM systems[J].Signal Processing,2012,28(3):392-396.
[14]CHEN B H,CUI Q M,YANG F,et al.A novel channel estimation method based on Kalman filter compressed sensing for time-varying OFDM system[C]//IEEE 6th International Conference on Wireless Communications and Signal Processing,2014:1-5.
[15]张晓东,董唯光,郭俊锋,等.基于αβ变换的压缩感知风电变流器电压信号压缩方法[J].广西大学学报(自然科学版),2016,41(6):1855-1862.ZHANG Xiaodong,DONG Weiguang,GUO Junfeng,et al.Wind power converter voltage signal compression method of compressed sensing based onαβtransform[J].Journal of Guangxi University(Natural Science Edition),2016,41(6):1855-1862.
[16]刘政,刘本永.基于图像深度信息的尺度不变特征变换算法误匹配点对剔除[J].计算机应用,2014,34(12):3554-3559.LIU Zheng,LIU Benyong.Removal of mismatches in scale-invariant feature transform algorithm using image depth information[J].Journal of Computer Applications,2014,34(12):3554-3559.
[17]LI H,GUO W B,SUN Z,et al.Adaptive Kalman filtered compressive sensing for streaming signals[C]//IEEE 78th Vehicular Technology Conference,2013:1-5.