This research looks at using AI/ML to achieve centimeter-level user
positioning in 6G applications such as the Industrial Internet of Things
(IIoT). Initial results show that our AI/ML-based method can estimate user
positions with an accuracy of 17 cm in an indoor factory environment. In this
proposal, we highlight our approaches and future directions.

使用人工智能和机器学习的方法，在 6G 应用中如物联网产业中，实现厘米级别的用户定位，首次结果表明我们的方法能够在室内工厂环境中将用户位置估计误差控制在 17 厘米以内。在本研究中，我们探讨了我们的方法和未来方向。

使用人工智能 / 机器学习实现 6G 应用的厘米级定位精度

Centimeter Positioning Accuracy using AI/ML for 6G Applications

In this paper, we consider the user positioning problem in the massive
multiple-input multiple-output (MIMO) orthogonal frequency-division
multiplexing (OFDM) system with a uniform planner antenna (UPA) array. Taking
advantage of the UPA array geometry and wide bandwidth, we advocate the use of
the angle-delay channel power matrix (ADCPM) as a new type of fingerprint to
replace the traditional ones. The ADCPM embeds the stable and stationary
multipath characteristics, e.g. delay, power, and angle in the vertical and
horizontal directions, which are beneficial to positioning. Taking ADCPM
fingerprints as the inputs, we propose a novel three-dimensional (3D)
convolution neural network (CNN) enabled learning method to localize users' 3D
positions. In particular, such a 3D CNN model consists of a convolution
refinement module to refine the elementary feature maps from the ADCPM
fingerprints, three extended Inception modules to extract the advanced feature
maps, and a regression module to estimate the 3D positions. By intensive
simulations, the proposed 3D CNN-enabled positioning method is demonstrated to
achieve higher positioning accuracy than the traditional searching-based ones,
with reduced computational complexity and storage overhead, and the ADCPM
fingerprints are more robust to noise contamination.

本文提出一种利用角度 - 延时信道功率矩阵（ADCPM）作为指纹的用户定位方法，并应用三维卷积神经网络模型来提高位置估计准确度，模拟结果表明该方法比传统基于搜索的方法具有更高的定位准确度，更强的噪声抗扰性。

学习本地化：大规模 MIMO-OFDM 系统中用户定位的 3D CNN 方法

Learning to Localize: A 3D CNN Approach to User Positioning in Massive  MIMO-OFDM Systems

We examine the usability of deep neural networks for multiple-input
multiple-output (MIMO) user positioning solely based on the orthogonal
frequency division multiplex (OFDM) complex channel coefficients. In contrast
to other indoor positioning systems (IPSs), the proposed method does not
require any additional piloting overhead or any other changes in the
communications system itself as it is deployed on top of an existing OFDM MIMO
system. Supported by actual measurements, we are mainly interested in the more
challenging non-line of sight (NLoS) scenario. However, gradient descent
optimization is known to require a large amount of data-points for training,
i.e., the required database would be too large when compared to conventional
methods. Thus, we propose a twostep training procedure, with training on
simulated line of sight (LoS) data in the first step, and finetuning on
measured NLoS positions in the second step. This turns out to reduce the
required measured training positions and thus, reduces the effort for data
acquisition.

本文研究了在现有 OFDM MIMO 系统中，仅基于正交频分复用（OFDM）复杂频道系数的深度神经网络在多输入多输出（MIMO）用户定位方面的可用性，提出了一种两步训练流程来减少所需的训练点数量。