Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks
Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks
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DOI：
10.1109/tsp.2016.2552504
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发表时间：
2015-10
影响因子：
5.4
通讯作者：
Alireza Sani;A. Vosoughi
中科院分区：
文献类型：
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作者：
Alireza Sani;A. Vosoughi
研究了非均匀无线传感器网络中高斯向量的分布式线性观测估计问题，其中融合中心（FC）利用线性估计器重构未知向量.传感器采用统一的多位量化器和二进制PSK调制，它们通过正交功率和带宽受限的无线信道与FC通信。作者研究发射功率和量化率（每传感器的比特数）分配方案，最大限度地减少均方误差（MSE）。特别是，他们推导出两个封闭形式的上界的MSE的优化参数，并提出“耦合”和“解耦”的资源分配方案，最大限度地减少这些界限。作者表明，界是很好的近似模拟MSE和所提出的方案的性能接近千里眼集中估计时，总的发射功率和带宽是非常大的。他们研究了功率和速率分配如何依赖于传感器的观测质量和信道增益以及总发射功率和带宽约束。他们的模拟证实了他们的分析结果，并证明了所提出的算法的上级性能。
The authors consider the distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, in which a fusion center (FC) reconstructs the unknown vector using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and they communicate with the FC over orthogonal power- and bandwidth-constrained wireless channels. The authors study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the mean-square error (MSE). In particular, they derive two closed-form upper bounds on the MSE in terms of the optimization parameters and propose “coupled” and “decoupled” resource allocation schemes that minimize these bounds. The authors show that the bounds are good approximations of the simulated MSE and that the performance of the proposed schemes approaches the clairvoyant centralized estimation when the total transmit power and bandwidth is very large. They investigate how the power and rate allocations are dependent on the sensors' observation qualities and channel gains and on the total transmit power and bandwidth constraints. Their simulations corroborate their analytical results and demonstrate the superior performance of the proposed algorithms.