Connectivity-Based Localization in Ultra-Dense Networks: CRLB, Theoretical Variance, and MLE

IEEE Access

Shan Luo¹  Kan Gu²  Tongtong Zhang²  Jiyan Huang²  Yalong Wang²  Jing Liang² 

[1] School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China;School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China;

关键词: Connectivity-based localization;    Cramer-Rao lower bound (CRLB);    theoretical variance;    maximum likelihood estimator (MLE);    centroid-based localization (CL);   

DOI  :  10.1109/ACCESS.2020.2974320

来源: DOAJ

【 摘 要 】

Performance analysis of connectivity-based geolocation in ultra-dense networks (UDNs) is a very important task. Although several performance analyses have been presented for range-free localization, determining the best achievable positioning accuracy of range-free localization remains an open problem. In this paper, we first derive the Cramer-Rao lower bound (CRLB) for the performance evaluation of range-free localization. All the current performance analyses in the literature for range-free localization are used to evaluate the real performance of a given algorithm, whereas the proposed CRLB provides a benchmark to evaluate the performance of any unbiased range-free location algorithm and determines the physical impossibility of the variance of an unbiased estimator being less than the bound. To the best of our knowledge, this is the first time in the literature that the CRLB for range-free localization has been derived. Second, the theoretical variance of centroid-based localization (CL) with an arbitrary node distribution is derived in this paper. In contrast to the existing theoretical variance of CL for uniform node distribution, the proposed theoretical variance can be used to evaluate the performance of CL in the case of an arbitrary node distribution. Additionally, characteristics of the proposed CRLB and theoretical variance are given in this paper. Finally, an optimal estimator based on a maximum likelihood estimator (MLE) is proposed to improve positioning accuracy. Since both prior information on the spatial node distribution and the connectivity property are effectively utilized in our algorithm, the proposed method performs better than the CL method and can asymptotically attain the CRLB.

【 授权许可】

Unknown