RSS-Based Multiple User Terminal Localization With Unknown Propagation Parameters in 6G Application

IF 1.4 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IET Signal Processing Pub Date : 2025-05-27 DOI:10.1049/sil2/5008754

Shoubin Zhang, Hongjun Wang, Zhexian Shen, Chao Chang, Xinhao Li

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Abstract

Since distributed sensing, storage, and computing are the frontiers for future sixth-generation (6G) communication systems, user terminal (UT) localization based on received signal strength (RSS) data from wireless sensor networks (WSNs) has received widespread attention because of its low energy consumption and ease of operation. Most of the existing work focused on the single-source localization problem. However, multiple UT localization is a more realistic problem that has not been well addressed. In this paper, we proposed a novel multiple UT localization scheme. Specifically, based on the log-normal property of spatial shadowing, the RSS is approximated as a random variable obeying a log-normal distribution, and the objective function is derived via maximum likelihood estimation. Then, aiming to better solve the objective function, a radio map is constructed to narrow search area, and a meta-heuristic algorithm with global search capability is adopted. Compared with the state-of-the-art methods through simulation experiments, it is proved that the method proposed in this paper has the best localization performance.

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6G应用中基于rss的未知传播参数多用户终端定位

由于分布式传感、存储和计算是未来第六代（6G）通信系统的前沿，基于无线传感器网络（WSNs）接收信号强度（RSS）数据的用户终端（UT）定位因其低能耗和易于操作而受到广泛关注。现有的大部分工作都集中在单源定位问题上。然而，多UT定位是一个更现实的问题，尚未得到很好的解决。本文提出了一种新的多UT定位方案。具体而言，基于空间阴影的对数正态性，将RSS近似为服从对数正态分布的随机变量，并通过极大似然估计导出目标函数。然后，为了更好地求解目标函数，构造无线电地图缩小搜索区域，采用具有全局搜索能力的元启发式算法；通过仿真实验与现有方法进行比较，证明了本文方法具有最佳的定位性能。

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来源期刊

IET Signal Processing 工程技术-工程：电子与电气

CiteScore

3.80

自引率

5.90%

发文量

审稿时长

9.5 months

期刊介绍： IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf