基于深度学习的室内LiFi用户位置和方向联合估计
Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation
摘要：
光保真（LiFi）是一种完全联网的双向光无线通信（OWC），被认为是用于高速室内连接的有前途的解决方案。与传统的射频无线系统不同，OWC信道不是各向同性的，这意味着设备方向会显著影响信道增益。然而，由于缺乏合适的LiFi系统信道模型，许多研究都假设接收器垂直向上并随机位于覆盖区域内，从实际角度来看，这并不是一个现实的假设。本文提出了室内LiFi系统的新型现实且基于测量的信道模型。准确地说，信道增益的统计数据是针对随机定向的固定和移动LiFi接收器的情况导出的。对于静止用户，提出了两种信道模型，即修正的截断拉普拉斯（MTL）模型和修正的Beta（MB）模型。针对LiFi用户，提出了两种信道模型，即修正截断高斯和模型（SMTG）和修正Beta和模型（SMB）。基于导出的模型，LiFi用户的随机方向和空间分布的影响进行了研究，其中我们表明，上述因素可以强烈影响信道增益和系统性能。
英文摘要：
Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) that is considered a promising solution for high-speed indoor connectivity. Unlike in conventional radio frequency wireless systems, the OWC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. However, due to the lack of proper channel models for LiFi systems, many studies have assumed that the receiver is vertically upward and randomly located within the coverage area, which is not a realistic assumption from a practical point of view. In this paper, novel realistic and measurement-based channel models for indoor LiFi systems are proposed. Precisely, the statistics of the channel gain are derived for the case of randomly oriented stationary and mobile LiFi receivers. For stationary users, two channel models are proposed, namely, the modified truncated Laplace (MTL) model and the modified Beta (MB) model. For LiFi users, two channel models are proposed, namely, the sum of modified truncated Gaussian (SMTG) model and the sum of modified Beta (SMB) model. Based on the derived models, the impact of random orientation and spatial distribution of LiFi users is investigated, where we show that the aforementioned factors can strongly affect the channel gain and system performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
