MSE Based Resource Optimization in Wireless Localization Networks

Cheng Yang, Fan Liu, Tingting Zhang
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引用次数: 1

Abstract

Proper resource allocation can improve the positioning accuracy, as well as the energy efficiency of wireless localization networks. Most existing investigations are carried out based on the Cramer Rao Lower Bound (CRLB), which is not always achievable, especially in low signal to noise ratio (SNR) regimes. In this paper, we mainly focus on the mean square error (MSE) achieved directly from various localization algorithms. Due to the fact that, MSE can not be handled in a closed form, learning based frameworks are thus provided. Aiming at the exponential increased state space in the multi-agent-scenario, low complexity alternating solutions are provided. In addition, a robust scheme is given considering the measurement error, which provide the solution for ranging links with clock deviation or obstruction. Numerical results including both simulations and practical experiments validate our analysis, and show great improvements of the proposed frameworks.
基于MSE的无线定位网络资源优化
合理的资源分配可以提高无线定位网络的定位精度和能源效率。大多数现有的研究都是基于Cramer Rao下限(CRLB)进行的,这并不总是可以实现的,特别是在低信噪比(SNR)的情况下。本文主要研究了各种定位算法直接得到的均方误差(MSE)。由于MSE不能以封闭的形式进行处理,因此提供了基于学习的框架。针对多智能体场景下状态空间呈指数增长的问题,给出了低复杂度的交替求解方案。此外,考虑到测量误差,给出了一种鲁棒方案,为存在时钟偏差或阻塞的测距链路提供了解决方案。包括仿真和实际实验在内的数值结果验证了我们的分析,并表明所提出的框架有很大的改进。
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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