535 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
Doctor of Philosophy
dissertationThis work seeks to improve upon existing methods for device-free localization (DFL) using radio frequency (RF) sensor networks. Device-free localization is the process of determining the location of a target object, typically a person, without the need for a device to be with the object to aid in localization. An RF sensor network measures changes to radio propagation caused by the presence of a person to locate that person. We show how existing methods which use either wideband or narrowband RF channels can be improved in ways including localization accuracy, energy efficiency, and system cost. We also show how wideband and narrowband systems can combine their information to improve localization. A common assumption in ultra-wideband research is that to estimate the bistatic delay or range, "background subtraction" is effective at removing clutter and must first be performed. Another assumption commonly made is that after background subtraction, each individual multipath component caused by a person's presence can be distinguished perfectly. We show that these assumptions are often not true and that ranging can still be performed even when these assumptions are not true. We propose modeling the difference between a current set of channel impulse responses (CIR) and a set of calibration CIRs as a hidden Markov model (HMM) and show the effectiveness of this model over background subtraction. The methods for performing device-free localization by using ultra-wideband (UWB) measurements and by using received signal strength (RSS) measurements are often considered separate topic of research and viewed only in isolation by two different communities of researchers. We consider both of these methods together and propose methods for combining the information obtained from UWB and RSS measurements. We show that using both methods in conjunction is more effective than either method on its own, especially in a setting where radio placement is constrained. It has been shown that for RSS-based DFL, measuring on multiple channels improves localization accuracy. We consider the trade-o s of measuring all radio links on all channels and the energy and latency expense of making the additional measurements required when sampling multiple channels. We also show the benefits of allowing multiple radios to transmit simultaneously, or in parallel, to better measure the available radio links
Cognitive radar network design and applications
PhD ThesisIn recent years, several emerging technologies in modern radar system
design are attracting the attention of radar researchers and practitioners
alike, noteworthy among which are multiple-input multiple-output
(MIMO), ultra wideband (UWB) and joint communication-radar technologies.
This thesis, in particular focuses upon a cognitive approach
to design these modern radars. In the existing literature, these technologies
have been implemented on a traditional platform in which the
transmitter and receiver subsystems are discrete and do not exchange
vital radar scene information. Although such radar architectures benefit
from these mentioned technological advances, their performance remains
sub-optimal due to the lack of exchange of dynamic radar scene
information between the subsystems. Consequently, such systems are
not capable to adapt their operational parameters “on the fly”, which
is in accordance with the dynamic radar environment. This thesis explores
the research gap of evaluating cognitive mechanisms, which could
enable modern radars to adapt their operational parameters like waveform,
power and spectrum by continually learning about the radar scene
through constant interactions with the environment and exchanging this
information between the radar transmitter and receiver. The cognitive
feedback between the receiver and transmitter subsystems is the facilitator
of intelligence for this type of architecture.
In this thesis, the cognitive architecture is fused together with modern
radar systems like MIMO, UWB and joint communication-radar designs
to achieve significant performance improvement in terms of target parameter
extraction. Specifically, in the context of MIMO radar, a novel
cognitive waveform optimization approach has been developed which facilitates
enhanced target signature extraction. In terms of UWB radar
system design, a novel cognitive illumination and target tracking algorithm
for target parameter extraction in indoor scenarios has been developed.
A cognitive system architecture and waveform design algorithm
has been proposed for joint communication-radar systems. This thesis
also explores the development of cognitive dynamic systems that allows
the fusion of cognitive radar and cognitive radio paradigms for optimal
resources allocation in wireless networks. In summary, the thesis provides
a theoretical framework for implementing cognitive mechanisms in
modern radar system design. Through such a novel approach, intelligent
illumination strategies could be devised, which enable the adaptation of
radar operational modes in accordance with the target scene variations
in real time. This leads to the development of radar systems which are
better aware of their surroundings and are able to quickly adapt to the
target scene variations in real time.Newcastle University, Newcastle upon Tyne:
University of Greenwich
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
An inclusive survey of contactless wireless sensing: a technology used for remotely monitoring vital signs has the potential to combating COVID-19
With the Coronavirus pandemic showing no signs of abating, companies and governments around the world are spending millions of dollars to develop contactless sensor technologies that minimize the need for physical interactions between the patient and healthcare providers. As a result, healthcare research studies are rapidly progressing towards discovering innovative contactless technologies, especially for infants and elderly people who are suffering from chronic diseases that require continuous, real-time control, and monitoring. The fusion between sensing technology and wireless communication has emerged as a strong research candidate choice because wearing sensor devices is not desirable by patients as they cause anxiety and discomfort. Furthermore, physical contact exacerbates the spread of contagious diseases which may lead to catastrophic consequences. For this reason, research has gone towards sensor-less or contactless technology, through sending wireless signals, then analyzing and processing the reflected signals using special techniques such as frequency modulated continuous wave (FMCW) or channel state information (CSI). Therefore, it becomes easy to monitor and measure the subject’s vital signs remotely without physical contact or asking them to wear sensor devices. In this paper, we overview and explore state-of-the-art research in the field of contactless sensor technology in medicine, where we explain, summarize, and classify a plethora of contactless sensor technologies and techniques with the highest impact on contactless healthcare. Moreover, we overview the enabling hardware technologies as well as discuss the main challenges faced by these systems.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E39
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