On the effect of FTDMA techniques on a distributed estimation in WSN

S. Narieda
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引用次数: 2

Abstract

This paper discusses an energy constrained distributed estimation in wireless sensor network (WSN) based on frequency and time division multiple access (FTDMA). In a measurement with WSN, sensors transmit observed quantized data to a fusion center (FC) via radio communication channels, to obtain a final estimate. Such systems require a multiple access technique such as time division multiple access, frequency division multiple access and FTDMA. In this paper, we focus on FTDMA as the multiple access technique, because of its high reliability and high spectral efficiency. We define an optimization problem to minimize a mean-squared error (MSE) while keeping a limited total power in FTDMA-based WSN, and derive the equations for bit allocation. Next, we investigate the effect of FTDMA-based measurement environment with WSN on the distributed estimation. Also, we show that a time slot and frequency band to be allocated to each sensor shall be optimized for the MSE minimization, because of the definition of the allocated power for sensors and the MSE. For this problem, we define a combination optimization to determine the time slot and frequency band. We develop the algorithm to determine the time slot, frequency band and allocated power to each sensor. In the algorithm, a genetic algorithm is employed to solve the combination optimization problem. Numerical examples are presented. Our developed algorithm outperforms conventional sensor selection with no optimization for the combination of sensors in terms of the MSE.
无线传感器网络中FTDMA技术对分布式估计的影响
讨论了一种基于频时分多址(FTDMA)的无线传感器网络(WSN)能量约束分布式估计方法。在无线传感器网络测量中,传感器将观测到的量化数据通过无线电通信信道传输到融合中心(FC),以获得最终的估计值。这种系统需要时分多址、频分多址和FTDMA等多址技术。由于FTDMA具有高可靠性和高频谱效率的特点,本文重点研究了该多址接入技术。在ftdma无线传感器网络中,我们定义了在保持有限总功率的情况下最小化均方误差(MSE)的优化问题,并推导了比特分配方程。其次,研究了基于ftdma的无线传感器网络测量环境对分布式估计的影响。此外,由于定义了传感器和MSE的分配功率,我们表明分配给每个传感器的时隙和频带应优化以最小化MSE。针对这一问题,我们定义了一个组合优化来确定时隙和频带。我们开发了一种算法来确定每个传感器的时隙、频带和分配功率。该算法采用遗传算法求解组合优化问题。给出了数值算例。我们开发的算法优于传统的传感器选择,在MSE方面没有对传感器组合进行优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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