Bellhop波束追踪方法利用射线理论在给定的几何和声源频率下能够得到准确的水下信道，但没有考虑到随机信道变化，本文将位置的不确定性和表面波浪的变化引起大尺度效应建模为1阶AR模型，理论建模分析并仿真验证了信道功率增益随通信带宽和通信距离的关系，采用最小二乘拟合对信道功率增益做局部平均，从而对时变水声信道概率密度函数进行估计。表面波浪的时变引起路径长度的变化，通过计算信道功率增益自相关函数可以估计模型参数，从而为信道预测提供可能。
Abstract
Bellhop beam tracking method can be used to get correct underwater acoustic channels under given geometry and sound source frequency with the ray theory, but it does not consider random channel changes. Here, the large-scale effect caused by position uncertainty and surface wave change was modeled as a first-order AR model. The relation among channel power gain, communication bandwidth and communication distance was theoretically modeled and analyzed, and this relation was verified with numerical simulation. The least square fitting was adopted to do local average of channel power gain, and then estimate the probability density function of a time-varying underwater acoustic channel. It was shown that time-varying surface waves can cause path length change; through calculating the auto-correlation function of channel power gain, the model parameters can be estimated to provide a possibility for channel prediction.
关键词
水声信道建模 /
AR模型 /
最小二乘拟合 /
统计特性
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Key words
underwater channel modeling /
AR model /
the least square fitting /
Statistical property
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参考文献
[1]. 姜喆，王海燕，赵瑞琴. 水声稀疏信道估计与大范围自适应平滑预测研究[J]. 西北工业大学学报. 2012, 32(10):844-851.
Jiang Zhe,Wang haiyan, Zhao ruiqin. Underwater acoustic sparse channel estimation and long-rang adaptive smooth prediction[J]. Journal of Xi'an Technological University. 2012,32(10):844-851.
[2]. 刘伯胜，雷佳煜. 水声学原理[M].哈尔滨工程大学出版社.2010.3
Liu bosheng，Lei jiayu. Principle of underwater acoustic[M]. Harbin Engineering University Press. 2010.3
[3]. A. Radosevic, T. Duman, J. Proakis. Channel prediction for adaptive modulation in underwater acoustic communications. in Proc. IEEE Oceans'11 Europe conference, Santander, Spain, June 2011.
[4]. W. Li and J. Preisig. Estimation of rapidly time-varying sparse channels. IEEE J. Ocean. Eng., vol. 32, no. 4, 927-939, Oct. 2007.
[5]. M. Stojanovic. Underwater acoustic communications: design considerations on the physical layer. IEEE/IFIP Fifth Annual Conference on Wireless On demand Network Systems and Services (WONS), Jan. 2008.
[6]. James C. Preisig. Performance analysis of adaptive equalization for coherent acoustic communications in the time-varying ocean environment. J. Acoust. Soc. Amer., vol.118, no. 1. 2005:267-278.
[7]. Mandar Chitre. A high-frequency warm shallow water acoustic communications channel model and measurements. J. Acoust. Soc. Amer., vol.122, no. 5.2580-2586. Nov. 2007.
[8]. P. Qarabaqi, M. Stojanovic. Modeling the large scale transmission loss in underwater acoustic channels. Proc. 49th Annu. Allerton Conf. Commun. Control Comput., Sep.2011.445-452.
[9]. M. B. Porter, "Bellhop code". available: http://oalib.hlsresearch.com/rays/index.html.
[10]. A. Radosevic, J. G. Proakis. Statistical characterization and capacity of shallow water acoustic channels. Proc. IEEE oceans' 09 conference, Bremen, Germany, May 2009.
[11]. P. Qarabaqi, M. Stojanovic. Statistical modeling of a shallow water acoustic communication channel. Proc. underwater acoustic measurements conference, Nafplion, Greece, June 2009.
[12]. P. Qarabaqi, M. Stojanovic. Statistical characterization and computationally efficient modeling of a class of underwater acoustic communication channels. IEEE Journal of oceanic engineering,vol.38, no.4, Oct.2013.701-716.
[13]. 马典军，葛万成. 基于统计自回归模型的时变信道均衡[J].电子技术. 2010,37(12):62-64;
Ma diancheng，Ge wangcheng. Time-varing channel equalization based on statistic auto regression model[J].Electronic Technology. 2010,37(12):62-64;
[14]. R. Ahmed, M. Stojanovic. Adaptive power control for underwater acoustic communications. OCEANS 2011 IEEE – Spain.
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