343 research outputs found
Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning
This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a
widely-used method of data transmission in wireless communications, when abrupt changes occur
in the channel. In highly mobile applications, new dynamics appear that might make channel
tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with
time. Simple examples might be the di erent channel dynamics a train receiver faces when it is
close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that
grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being
reborn, and so tap birth-death detection is of the essence.
In order to improve the quality of communications, we delved into mathematical methods to
detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/
Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances
in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt
change detection by informing and inspiring the creation of low-complexity implementations for
real-world channel tracking. In particular, two such novel trackers were created: the Simpli-
ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF)
schemes.
The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies
the three following heuristics for tap birth-death detection: a) detect death if the tap gain
jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly
converged into approximately zero (memory detection); c) detect birth if the tap gain is far from
zero.
The precise parameters for these three simple rules can be approximated with simple theoretical
derivations and then ne-tuned through extensive simulations. The status detector for each
tap using only these three computationally inexpensive threshold comparisons achieves an error
reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations.
This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise
Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems.
The underlying RST framework for the SMAP was then extended to combined death/birth
and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal
SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed
SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate
SNR detection.
The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap
birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show
that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy.
These proposed Neural-Kalman schemes can work as novel trackers for multipath channels,
since they are robust to wide variations in the probabilities of tap birth and death. Such robustness
suggests a single, low-complexity NNKF could be reusable over di erent tap indices and
communication environments.
Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from
one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to
model, detect and track such changes, providing a geometric justi cation for this and additional
non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks
and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive
literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring
campaigns is included.
For this generalized framework of abrupt channel changes that includes partial tap lateral
hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation
results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for
integration in NNKF schemes.
Finally, the newly found understanding of abrupt changes and the interactions between Kalman
lters and neural networks is leveraged to analyze the neural consequences of abrupt changes
and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract
some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new
portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts
(HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic
stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process.
The forecasting problem in the presence of expert disagreements is illustrated with a hopping
LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually
solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming
and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert
disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis
suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and
social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their
existence in the areas of neuroscience and nance.
Apart from providing speci c examples, practical guidelines and historical (out)performance
for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural-
Kalman architecture for ever granular stereotyping providing a practical solution for continual
learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications
and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana GarcĂa Armada.- Vocal: JosĂ© Antonio Portilla Figuera
Ubiquitous Indoor Fine-Grained Positioning and Tracking: A Channel Response Perspective
The future of location-aided applications is shaped by the ubiquity of
Internet-of-Things devices. As an increasing amount of commercial off-the-shelf
radio devices support channel response collection, it is possible to achieve
fine-grained position estimation at a relatively low cost. In this paper, we
focus on the channel response-based positioning and tracking for various
applications. We first give an overview of the state of the art (SOTA) of
channel response-enabled localization, which is further classified into two
categories, i.e., device-based and contact-free schemes. A taxonomy for these
complementary approaches is provided concerning the involved techniques. Then,
we present a micro-benchmark of channel response-based direct positioning and
tracking for both device-based and contact-free schemes. Finally, some
practical issues for real-world applications and future research opportunities
are pointed out.Comment: 13th International Conference on Indoor Positioning and Indoor
Navigatio
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
On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection
This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment
Towards joint communication and sensing (Chapter 4)
Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed
Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information
The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively
Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information
The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively
Novel Hybrid-Learning Algorithms for Improved Millimeter-Wave Imaging Systems
Increasing attention is being paid to millimeter-wave (mmWave), 30 GHz to 300
GHz, and terahertz (THz), 300 GHz to 10 THz, sensing applications including
security sensing, industrial packaging, medical imaging, and non-destructive
testing. Traditional methods for perception and imaging are challenged by novel
data-driven algorithms that offer improved resolution, localization, and
detection rates. Over the past decade, deep learning technology has garnered
substantial popularity, particularly in perception and computer vision
applications. Whereas conventional signal processing techniques are more easily
generalized to various applications, hybrid approaches where signal processing
and learning-based algorithms are interleaved pose a promising compromise
between performance and generalizability. Furthermore, such hybrid algorithms
improve model training by leveraging the known characteristics of radio
frequency (RF) waveforms, thus yielding more efficiently trained deep learning
algorithms and offering higher performance than conventional methods. This
dissertation introduces novel hybrid-learning algorithms for improved mmWave
imaging systems applicable to a host of problems in perception and sensing.
Various problem spaces are explored, including static and dynamic gesture
classification; precise hand localization for human computer interaction;
high-resolution near-field mmWave imaging using forward synthetic aperture
radar (SAR); SAR under irregular scanning geometries; mmWave image
super-resolution using deep neural network (DNN) and Vision Transformer (ViT)
architectures; and data-level multiband radar fusion using a novel
hybrid-learning architecture. Furthermore, we introduce several novel
approaches for deep learning model training and dataset synthesis.Comment: PhD Dissertation Submitted to UTD ECE Departmen
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