12 research outputs found
Channel Estimation for Ambient Backscatter Communication Systems with Massive-Antenna Reader
Ambient backscatter, an emerging green communication technology, has aroused
great interest from both academia and industry. One open problem for ambient
backscatter communication (AmBC) systems is channel estimation for a
massive-antenna reader. In this paper, we focus on channel estimation problem
in AmBC systems with uniform linear array (ULA) at the reader which consists of
large number of antennas. We first design a two-step method to jointly estimate
channel gains and direction of arrivals (DoAs), and then refine the estimates
through angular rotation. Additionally, Cramer-Rao lower bounds (CRLBs) are
derived for both the modulus of the channel gain and the DoA estimates.
Simulations are then provided to validate the analysis, and to show the
efficiency of the proposed approach.Comment: 5 figures, submitted to IEEE Transactions on Vehicular Technology, 29
March, 201
Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications
This article introduces a novel lightweight framework using ambient
backscattering communications to counter eavesdroppers. In particular, our
framework divides an original message into two parts: (i) the active-transmit
message transmitted by the transmitter using conventional RF signals and (ii)
the backscatter message transmitted by an ambient backscatter tag that
backscatters upon the active signals emitted by the transmitter. Notably, the
backscatter tag does not generate its own signal, making it difficult for an
eavesdropper to detect the backscattered signals unless they have prior
knowledge of the system. Here, we assume that without decoding/knowing the
backscatter message, the eavesdropper is unable to decode the original message.
Even in scenarios where the eavesdropper can capture both messages,
reconstructing the original message is a complex task without understanding the
intricacies of the message-splitting mechanism. A challenge in our proposed
framework is to effectively decode the backscattered signals at the receiver,
often accomplished using the maximum likelihood (MLK) approach. However, such a
method may require a complex mathematical model together with perfect channel
state information (CSI). To address this issue, we develop a novel deep
meta-learning-based signal detector that can not only effectively decode the
weak backscattered signals without requiring perfect CSI but also quickly adapt
to a new wireless environment with very little knowledge. Simulation results
show that our proposed learning approach, without requiring perfect CSI and
complex mathematical model, can achieve a bit error ratio close to that of the
MLK-based approach. They also clearly show the efficiency of the proposed
approach in dealing with eavesdropping attacks and the lack of training data
for deep learning models in practical scenarios
A survey of symbiotic radio: Methodologies, applications, and future directions
The sixth generation (6G) wireless technology aims to achieve global connectivity with environmentally sustainable networks to improve the overall quality of life. The driving force behind these networks is the rapid evolution of the Internet of Things (IoT), which has led to a proliferation of wireless applications across various domains through the massive deployment of IoT devices. The major challenge is to support these devices with limited radio spectrum and energy-efficient communication. Symbiotic radio (SRad) technology is a promising solution that enables cooperative resource-sharing among radio systems through symbiotic relationships. By fostering mutualistic and competitive resource sharing, SRad technology enables the achievement of both common and individual objectives among the different systems. It is a cutting-edge approach that allows for the creation of new paradigms and efficient resource sharing and management. In this article, we present a detailed survey of SRad with the goal of offering valuable insights for future research and applications. To achieve this, we delve into the fundamental concepts of SRad technology, including radio symbiosis and its symbiotic relationships for coexistence and resource sharing among radio systems. We then review the state-of-the-art methodologies in-depth and introduce potential applications. Finally, we identify and discuss the open challenges and future research directions in this field
Optimal Channel Estimation for Reciprocity-Based Backscattering with a Full-Duplex MIMO Reader
Backscatter communication (BSC) technology can enable ubiquitous deployment
of low-cost sustainable wireless devices. In this work we investigate the
efficacy of a full-duplex multiple-input-multiple-output (MIMO) reader for
enhancing the limited communication range of monostatic BSC systems. As this
performance is strongly influenced by the channel estimation (CE) quality, we
first derive a novel least-squares estimator for the forward and backward links
between the reader and the tag, assuming that reciprocity holds and K
orthogonal pilots are transmitted from the first K antennas of an N antenna
reader. We also obtain the corresponding linear minimum-mean square-error
estimate for the backscattered channel. After defining the transceiver design
at the reader using these estimates, we jointly optimize the number of
orthogonal pilots and energy allocation for the CE and information decoding
phases to maximize the average backscattered signal-to-noise ratio (SNR) for
efficiently decoding the tag's messages. The unimodality of this SNR in
optimization variables along with a tight analytical approximation for the
jointly global optimal design is also discoursed. Lastly, the selected
numerical results validate the proposed analysis, present key insights into the
optimal resource utilization at reader, and quantify the achievable gains over
the benchmark schemes.Comment: accepted for publication in IEEE Transactions on Signal Processing,
16 pages, 15 figures, 1 tabl