3,717 research outputs found
Spatial Modulation for Ambient Backscatter Communications: Modeling and Analysis
Multiple-antenna backscatter is emerging as a promising approach to offer
high communication performance for the data-intensive applications of ambient
backscatter communications (AmBC). Although much has been understood about
multiple-antenna backscatter in conventional backscatter communications (CoBC),
existing analytical models cannot be directly applied to AmBC due to the
structural differences in RF source and tag circuit designs. This paper takes
the first step to fill the gap, by exploring the use of spatial modulation (SM)
in AmBC whenever tags are equipped with multiple antennas. Specifically, we
present a practical multiple-antenna backscatter design for AmBC that exempts
tags from the inter-antenna synchronization and mutual coupling problems while
ensuring high spectral efficiency and ultra-low power consumption. We obtain an
optimal detector for the joint detection of both backscatter signal and source
signal based on the maximum likelihood principle. We also design a two-step
algorithm to derive bounds on the bit error rate (BER) of both signals.
Simulation results validate the analysis and show that the proposed scheme can
significantly improve the throughput compared with traditional systems.Comment: The system model and some simulation parameters of this article need
to be reconsidered and improve
Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
Tag signal detection is one of the key tasks in ambient backscatter
communication (AmBC) systems. However, obtaining perfect channel state
information (CSI) is challenging and costly, which makes AmBC systems suffer
from a high bit error rate (BER). To eliminate the requirement of channel
estimation and to improve the system performance, in this paper, we adopt a
deep transfer learning (DTL) approach to implicitly extract the features of
channel and directly recover tag symbols. To this end, we develop a DTL
detection framework which consists of offline learning, transfer learning, and
online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is
derived based on the minimum error probability (MEP) criterion. As a
realization of the developed framework, we then apply convolutional neural
networks (CNN) to intelligently explore the features of the sample covariance
matrix, which facilitates the design of a CNN-based algorithm for tag signal
detection. Exploiting the powerful capability of CNN in extracting features of
data in the matrix formation, the proposed method is able to further improve
the system performance. In addition, an asymptotic explicit expression is also
derived to characterize the properties of the proposed CNN-based method when
the number of samples is sufficiently large. Finally, extensive simulation
results demonstrate that the BER performance of the proposed method is
comparable to that of the optimal detection method with perfect CSI.Comment: Accepted by IEEE Transactions on Wireless Communication
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Existing tag signal detection algorithms inevitably suffer from a high bit
error rate (BER) due to the difficulties in estimating the channel state
information (CSI). To eliminate the requirement of channel estimation and to
improve the system performance, in this paper, we adopt a deep transfer
learning (DTL) approach to implicitly extract the features of communication
channel and directly recover tag symbols. Inspired by the powerful capability
of convolutional neural networks (CNN) in exploring the features of data in a
matrix form, we design a novel covariance matrix aware neural network
(CMNet)-based detection scheme to facilitate DTL for tag signal detection,
which consists of offline learning, transfer learning, and online detection.
Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based
on the minimum error probability (MEP) criterion. Taking advantage of the
outstanding performance of DTL in transferring knowledge with only a few
training data, the proposed scheme can adaptively fine-tune the detector for
different channel environments to further improve the detection performance.
Finally, extensive simulation results demonstrate that the BER performance of
the proposed method is comparable to that of the optimal detection method with
perfect CSI.Comment: Accepted by IEEE Globecom 2020; Journal version "Deep Transfer
Learning for Signal Detection in Ambient Backscatter Communications" has been
accepted by IEEE TWC. arXiv admin note: substantial text overlap with
arXiv:2009.0523
On Measurement of the Spatio-Frequency Property of OFDM Backscattering
Orthogonal frequency-division multiplexing (OFDM) backscatter system, such as
Wi-Fi backscatter, has recently been recognized as a promising technique for
the IoT connectivity, due to its ubiquitous and low-cost property. This paper
investigates the spatial-frequency property of the OFDM backscatter which takes
the distance and the angle into account in different frequency bands. We deploy
three typical scenarios for performing measurements to evaluate the received
signal strength from the backscatter link. The impact of the distances among
the transmitter, the tag and the receiver, as well as the angle between the
transmitter and the tag is observed through the obtained measurement data. From
the evaluation results, it is found that the best location of tag is either
close to the receiver or the transmitter which depends on the frequency band,
and the best angle is 90 degrees between the transmitter and the receiver. This
work opens the shed light on the spatial deployment of the backscatter tag in
different frequency band with the aim of improving the performance and reducing
the interference
Ambient Backscatter Communications: A Contemporary Survey
Recently, ambient backscatter communications has been introduced as a
cutting-edge technology which enables smart devices to communicate by utilizing
ambient radio frequency (RF) signals without requiring active RF transmission.
This technology is especially effective in addressing communication and energy
efficiency problems for low-power communications systems such as sensor
networks. It is expected to realize numerous Internet-of-Things (IoT)
applications. Therefore, this paper aims to provide a contemporary and
comprehensive literature review on fundamentals, applications, challenges, and
research efforts/progress of ambient backscatter communications. In particular,
we first present fundamentals of backscatter communications and briefly review
bistatic backscatter communications systems. Then, the general architecture,
advantages, and solutions to address existing issues and limitations of ambient
backscatter communications systems are discussed. Additionally, emerging
applications of ambient backscatter communications are highlighted. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 18 figures, journa
Robot-assisted Backscatter Localization for IoT Applications
Recent years have witnessed the rapid proliferation of backscatter
technologies that realize the ubiquitous and long-term connectivity to empower
smart cities and smart homes. Localizing such backscatter tags is crucial for
IoT-based smart applications. However, current backscatter localization systems
require prior knowledge of the site, either a map or landmarks with known
positions, which is laborious for deployment. To empower universal localization
service, this paper presents Rover, an indoor localization system that
localizes multiple backscatter tags without any start-up cost using a robot
equipped with inertial sensors. Rover runs in a joint optimization framework,
fusing measurements from backscattered WiFi signals and inertial sensors to
simultaneously estimate the locations of both the robot and the connected tags.
Our design addresses practical issues including interference among multiple
tags, real-time processing, as well as the data marginalization problem in
dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi
chips and customized backscatter tags. Our experiments show that Rover achieves
localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.Comment: To appear in IEEE Transactions on Wireless Communications. arXiv
admin note: substantial text overlap with arXiv:1908.0329
Interference-Free Transceiver Design and Signal Detection for Ambient Backscatter Communication Systems over Frequency-Selective Channels
In this letter, we study the ambient backscatter communication systems over
frequency-selective channels. Specifically, we propose an interference-free
transceiver design to facilitate signal detection at the reader. Our design
utilizes the cyclic prefix (CP) of orthogonal frequency-division multiplexing
(OFDM) source symbols, which can cancel the signal interference and thus
enhance the detection accuracy at the reader. Meanwhile, our design results in
no interference on the existing OFDM communication systems. We also suggest a
maximum likelihood (ML) detector for the reader and derive two detection
thresholds. Simulations are then provided to corroborate our proposed studies.Comment: 4 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1901.0036
Transceiver Design for Ambient Backscatter Communication over Frequency-Selective Channels
Existing studies about ambient backscatter communication mostly assume
flat-fading channels. However, frequency-selective channels widely exist in
many practical scenarios. Therefore, this paper investigates ambient
backscatter communication systems over frequency-selective channels. In
particular, we propose an interference-free transceiver design to facilitate
signal detection at the reader. Our design utilizes the cyclic prefix (CP) of
orthogonal frequency-division multiplexing (OFDM) source symbols, which can
cancel the signal interference and thus enhance the detection accuracy at the
reader. Meanwhile, our design leads to no interference on the existing OFDM
communication systems. Next we suggest a chi-square based detector for the
reader and derive the optimal detection threshold. Simulations are then
provided to corroborate our proposed studies.Comment: 5 pages, 5 figures. arXiv admin note: substantial text overlap with
arXiv:1812.1127
From Communication to Sensing : Recognizing and Counting Repetitive Motions with Wireless Backscattering
Recently several ground-breaking RF-based motion recognition systems were
proposed to detect and/or recognize macro/micro human movements. These systems
often suffer from various interferences caused by multiple-users moving
simultaneously, resulting in extremely low recognition accuracy. To tackle this
challenge, we propose a novel system, called Motion-Fi, which marries battery
free wireless backscattering and device-free sensing. Motion-Fi is an accurate,
interference tolerable motion-recognition system, which counts repetitive
motions without using scenario-dependent templates or profiles and enables
multi-users performing certain motions simultaneously because of the relatively
short transmission range of backscattered signals. Although the repetitive
motions are fairly well detectable through the backscattering signals in
theory, in reality they get blended into various other system noises during the
motion. Moreover, irregular motion patterns among users will lead to expensive
computation cost for motion recognition. We build a backscattering wireless
platform to validate our design in various scenarios for over 6 months when
different persons, distances and orientations are incorporated. In our
experiments, the periodicity in motions could be recognized without any
learning or training process, and the accuracy of counting such motions can be
achieved within 5% count error. With little efforts in learning the patterns,
our method could achieve 93.1% motion-recognition accuracy for a variety of
motions. Moreover, by leveraging the periodicity of motions, the recognition
accuracy could be further improved to nearly 100% with only 3 repetitions. Our
experiments also show that the motions of multiple persons separating by around
2 meters cause little accuracy reduction in the counting process
Unitary Query for the MIMO Backscatter RFID Channel
A MIMO backscatter RFID system consists of three operational ends: the query
end (with reader transmitting antennas), the tag end (with tag
antennas) and the receiving end (with reader receiving antennas). Such an
setting in RFID can bring spatial diversity and has been
studied for STC at the tag end. Current understanding of the query end is that
it is only an energy provider for the tag and query signal designs cannot
improve the performance. However, we propose a novel \textit{unitary query}
scheme, which creates time diversity \emph{within channel coherent time} and
can yield \emph{significant} performance improvements. To overcome the
difficulty of evaluating the performance when the unitary query is employed at
the query end and STC is employed at the tag end, we derive a new measure based
on the ranks of certain carefully constructed matrices. The measure implies
that the unitary query has superior performance. Simulations show that the
unitary query can bring dB gain in mid SNR regimes. In addition, the
unitary query can also improve the performance of single-antenna tags
significantly, allowing employing low complex and small-size single-antenna
tags for high performance. This improvement is unachievable for single-antenna
tags when the conventional uniform query is employed
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