Online Modeling and Prediction of the Large-Scale Temporal Variation in Underwater Acoustic Communication Channels
Online Modeling and Prediction of the Large-Scale Temporal Variation in Underwater Acoustic Communication Channels
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DOI：
10.1109/access.2018.2882890
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发表时间：
2018-11
期刊：
影响因子：
3.9
通讯作者：
Wensheng Sun;Zhaohui Wang
中科院分区：
文献类型：
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作者：
Wensheng Sun;Zhaohui Wang
受环境条件的影响，水声通信信道在不同时间尺度上表现出动态变化。现有研究已广泛研究了短传输持续时间内的信道动态。在本文中，我们通过利用其固有的时间相关性和与水环境条件的相关性，研究长期缓慢变化的河道参数的在线建模和预测。这些参数的示例包括传输内的本地平均信道属性，例如平均信道增益与噪声功率比、快衰落统计数据、平均延迟扩展和平均多普勒扩展。本文采用数据驱动的视角，将感兴趣的缓慢变化的通道参数的时间演化建模为时不变分量、可以由可用环境参数明确表示的时变过程以及描述未知或不可测量的物理机制的贡献的马尔可夫潜在过程的总和。开发了一种算法，基于实时连续收集的信道测量值和环境参数，递归估计未知模型参数并预测感兴趣的信道参数。我们通过引入乘法季节性自回归过程来对季节性相关性进行建模，进一步将上述模型和递归算法扩展到表现出周期性（又称季节性）动态的通道。所提出的模型和算法通过广泛的模拟和来自两个浅水实验的数据集进行评估。实验结果表明，可以很好地预测平均信道增益噪声功率比、快衰落统计数据和平均延迟扩展。
Influenced by environmental conditions, underwater acoustic communication channels exhibit dynamics on various time scales. The channel dynamics within a short transmission duration have been extensively studied in existing research. In this paper, we investigate online modeling and prediction of slowly-varying channel parameters in a long term, by exploiting their inherent temporal correlation and correlation with water environmental conditions. Examples of those parameters include the locally-averaged channel properties within a transmission, such as the average channel-gain-to-noise-power ratio, the fast fading statistics, the average delay spread, and the average Doppler spread. Adopting a data-driven perspective, this paper models the temporal evolution of a slowly-varying channel parameter of interest as the summation of a time-invariant component, a time-varying process that can be explicitly represented by available environmental parameters, and a Markov latent process that describes the contribution from unknown or unmeasurable physical mechanisms. An algorithm is developed to recursively estimate the unknown model parameters and predict the channel parameter of interest, based on the sequentially collected channel measurements and environmental parameters in real time. We further extend the above model and the recursive algorithm to channels that exhibit periodic (a.k.a. seasonal) dynamics, by introducing a multiplicative seasonal autoregressive process to model the seasonal correlation. The proposed models and algorithms are evaluated via extensive simulations and data sets from two shallow-water experiments. The experimental results reveal that the average channel-gain-to-noise-power ratio, the fast fading statistics, and the average delay spread can be well predicted.