Recently, researchers have been exploring various ways to improve the
effectiveness and efficiency of autonomous vehicles by researching new methods,
especially for indoor scenarios. Autonomous Vehicles in indoor navigation
systems possess many challenges especially the limited accuracy of GPS in
indoor scenarios. Several, robust methods have been explored for autonomous
vehicles in indoor scenarios to solve this problem, but the ineffectiveness of
the proposed methods is the high deployment cost. To address the
above-mentioned problems we have presented A low-cost indoor navigation method
for autonomous vehicles called Affordable Indoor Navigation Solution (AINS)
which is based on based on Monocular Camera. Our proposed solution is mainly
based on a mono camera without relying on various huge or power-inefficient
sensors to find the path, such as range finders and other navigation sensors.
Our proposed method shows that we can deploy autonomous vehicles indoor
navigation systems while taking into consideration the cost. We can observe
that the results shown by our solution are better than existing solutions and
we can reduce the estimated error and time consumption.

提出了一种低成本的室内导航方法，基于单眼相机技术，能够改善自主车辆在室内环境下的导航效果和效率，并且相比其他方法具有更好的结果和减少的估计误差和时间消耗。

AINS：借助单目摄像头通过线颜色识别实现自主车辆的经济室内导航解决方案

AINS: Affordable Indoor Navigation Solution via Line Color  Identification Using Mono-Camera for Autonomous Vehicles

Radio-frequency coverage maps (RF maps) are extensively utilized in wireless
networks for capacity planning, placement of access points and base stations,
localization, and coverage estimation. Conducting site surveys to obtain RF
maps is labor-intensive and sometimes not feasible. In this paper, we propose
radio-frequency adversarial deep-learning inference for automated network
coverage estimation (RADIANCE), a generative adversarial network (GAN) based
approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a
semantic map, a high-level representation of the indoor environment to encode
spatial relationships and attributes of objects within the environment and
guide the RF map generation process. We introduce a new gradient-based loss
function that computes the magnitude and direction of change in received signal
strength (RSS) values from a point within the environment. RADIANCE
incorporates this loss function along with the antenna pattern to capture
signal propagation within a given indoor configuration and generate new
patterns under new configuration, antenna (beam) pattern, and center frequency.
Extensive simulations are conducted to compare RADIANCE with ray-tracing
simulations of RF maps. Our results show that RADIANCE achieves a mean average
error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak
signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity
index (MS-SSIM) of 0.80.

利用生成对抗网络的方法以及语义地图对室内环境进行建模，实现射频地图的自动合成及网络覆盖估计。

RADIANCE：用于自动网络覆盖估计的射频对抗深度学习推断

RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for  Automated Network Coverage Estimation

Millimeter-wave (mmWave) and Terahertz (THz) will be used in the
sixth-generation (6G) wireless systems, especially for indoor scenarios. This
paper presents an indoor three-dimensional (3-D) statistical channel model for
mmWave and sub-THz frequencies, which is developed from extensive channel
propagation measurements conducted in an office building at 28 GHz and 140 GHz
in 2014 and 2019. Over 15,000 power delay profiles (PDPs) were recorded to
study channel statistics such as the number of time clusters, cluster delays,
and cluster powers. All the parameters required in the channel generation
procedure are derived from empirical measurement data for 28 GHz and 140 GHz
line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The channel model
is validated by showing that the simulated root mean square (RMS) delay spread
and RMS angular spread yield good agreements with measured values. An indoor
channel simulation software is built upon the popular NYUSIM outdoor channel
simulator, which can generate realistic channel impulse response, PDP, and
power angular spectrum.

本文介绍了一个基于广泛的 28 GHz 和 140 GHz 的信道测量开发的室内三维统计信道模型，用于未来的第六代无线系统，特别是为室内场景设计。