A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large.

中文翻译：

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无线传感器网络中用于参数估计的优化传输

模拟无线传感器网络中的一个主要问题是设计传感器节点的增益或相移（即中继配置），以便在融合中心或更普遍地在每个融合中心实现对某些关注参数的准确估计节点采用分布式参数估计方案。在本文中，通过使用原始设计问题的过度参数化，我们设计了一种循环优化方法，即使在涉及传感器选择或离散相移的复杂情况下，也可以处理传感器节点的增益和相移的调整。所提出的设计框架的每个迭代都由Gram-Schmidt过程和类似于幂方法的迭代组成，因此，计算成本较低。除了提出融合中心的设计问题之外，我们进一步提出了一种基于共识的框架，用于分布式网络中确定性参数的分散估计，这会导致类似的传感器增益设计问题。数值结果证实了与现有技术方法相比所建议方法的计算优势-当传感器网络变大时，这一优势将更加明显。