Acoustic Beam Characterization and Selection for Optimized Underwater Communication
Acoustic Beam Characterization and Selection for Optimized Underwater Communication
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DOI：
10.3390/app9132740
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发表时间：
2019-07-01
影响因子：
2.7
通讯作者：
Younis, Mohamed
中科院分区：
文献类型：
--
作者：
Ahmed, Akram;Younis, Mohamed
为了提高水声信号的可探测性并节省能量，节点利用定向传输。此外，节点在被归类为非均匀的三维(3D)环境中工作，其中传播信号基于观测到的声速分布(SSP)改变其方向。将3D定向传输与频繁的节点漂移和变化的水下SSP相结合，使得选择合适的传输角度来维护水下通信链路的过程变得复杂。从根本上说，在节点漂移时利用定向传输会导致已建立的通信链路中断，因此节点需要找到新的角度来重新建立这些链路。此外，选择任意的传输角度可能会导致波束重叠或导致水下区域未被覆盖。为了解决上述问题，本文提出了一种自主波束选择方法，通过选择不重叠的波束来优化水下通信，同时减少丢失区域的可能性，即最大化覆盖。这种优化是通过利用考虑所用换能器的能力的结构化角度选择机构来实现的。此外，我们还提出了一种适用于资源受限节点的光线分类算法。然后，我们将水下介质划分为区域，每个区域通过每种射线类型的覆盖范围的限制来识别。最后，我们利用这些区域的限制来帮助节点选择最好的射线来重建与漂移节点的通信。我们通过模拟来验证我们的贡献，其中利用实际的SSP来验证波束分类过程。
To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process.