Gao Yanbiao, Deng Zhongliang, Huo Yuqi, Chen Wenyan
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2024 Oct 9;24(19):6494. doi: 10.3390/s24196494.
Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.
定位服务是一项关键技术，它将物理世界与数字信息连接起来，显著提高了生活和工作中的效率与便利性。5G技术的发展证明，定位服务是当前和未来蜂窝网络的重要组成部分。然而，非视距（NLoS）传播阻碍了定位精度，严重影响了角度和延迟的测量。在本研究中，我们引入了一种深度自动编码信道变换生成对抗网络模型，该模型将视距（LoS）样本用作单一类别训练集，以充分提取LoS的潜在特征，最终使用鉴别器作为NLoS标识符。我们通过评估其在不同场景下的泛化能力，在5G室内和室内工厂（密集 clutter，低基站）场景中验证了所提出的模型。结果表明，与现有最先进方法相比，所提出的模型显著减少了设备资源的使用，曲线下面积提高了2.15%，同时计算时间减少了12.6%。这种方法有望部署在未来的定位终端中，以实现卓越的定位精度，满足商业和工业物联网应用的需求。
