102 research outputs found
Localization Techniques in Multiple-Input Multiple-Output Communication: Fundamental Principles, Challenges, and Opportunities
This chapter provides an overview of localization techniques in Multiple-Input Multiple-Output (MIMO) communication systems. The chapter mainly focuses on sub-6 GHz and mmWave bands. MIMO technology enables high-capacity wireless communication, but also presents challenges for localization due to the complexity of the signal propagation environment. Various methods have been developed to overcome these challenges, which utilize side information such as the map of the area, or techniques such as Compressive Sensing (CS), Deep Learning (DL), Gaussian Process Regression (GPR), or clustering. These techniques utilize wireless communication parameters such as Received Signal Strength Indicator (RSSI), Channel State Information (CSI), Angle-Delay-Profile (ADP), Angle-of-Departure (AoD), Angle-of-Arrival (AoA), or Time-of-Arrival (ToA) as inputs to estimate the user’s location. The goal of this chapter is to offer a comprehensive understanding of MIMO localization techniques, along with an overview of the challenges and opportunities associated with them. Furthermore, it also aims to provide the theoretical background on channel models and wireless channel parameters required to understand the localization techniques
Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation
Radio signals are used broadly as navigation aids, and current and future
terrestrial wireless communication systems have properties that make their
dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable
widespread coverage for data communication and navigation, but typically offer
smaller bandwidths and limited resolution for precise estimation of geometries,
particularly in environments where propagation channels are diffuse in time
and/or space. Non-parametric methods have been employed with some success for
such scenarios both commercially and in literature, but often with an emphasis
on low-cost hardware and simple models of propagation, or with simulations that
do not fully capture hardware impairments and complex propagation mechanisms.
In this article, we make opportunistic observations of downlink signals
transmitted by commercial cellular networks by using a software-defined radio
and massive antenna array mounted on a passenger vehicle in an urban non
line-of-sight scenario, together with a ground truth reference for vehicle
pose. With these observations as inputs, we employ artificial neural networks
to generate estimates of vehicle location and heading for various artificial
neural network architectures and different representations of the input
observation data, which we call wiometrics, and compare the performance for
navigation. Position accuracy on the order of a few meters, and heading
accuracy of a few degrees, are achieved for the best-performing combinations of
networks and wiometrics. Based on the results of the experiments we draw
conclusions regarding possible future directions for wireless navigation using
statistical methods
CSI-fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network
Deep learning has been widely adopted for channel state information
(CSI)-fingerprinting indoor localization systems. These systems usually consist
of two main parts, i.e., a positioning network that learns the mapping from
high-dimensional CSI to physical locations and a tracking system that utilizes
historical CSI to reduce the positioning error. This paper presents a new
localization system with high accuracy and generality. On the one hand, the
receptive field of the existing convolutional neural network (CNN)-based
positioning networks is limited, restricting their performance as useful
information in CSI is not explored thoroughly. As a solution, we propose a
novel attention-augmented residual CNN to utilize the local information and
global context in CSI exhaustively. On the other hand, considering the
generality of a tracking system, we decouple the tracking system from the CSI
environments so that one tracking system for all environments becomes possible.
Specifically, we remodel the tracking problem as a denoising task and solve it
with deep trajectory prior. Furthermore, we investigate how the precision
difference of inertial measurement units will adversely affect the tracking
performance and adopt plug-and-play to solve the precision difference problem.
Experiments show the superiority of our methods over existing approaches in
performance and generality improvement.Comment: 32 pages, Added references in section 2,3; Added explanations for
some academic terms; Corrected typos; Added experiments in section 5,
previous results unchanged; is under review for possible publicatio
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