147 research outputs found
Time-Spread Pilot-Based Channel Estimation for Backscatter Networks
Current backscatter channel estimators employ an inefficient silent pilot
transmission protocol, where tags alternate between silent and active states.
To enhance performance, we propose a novel approach where tags remain active
simultaneously throughout the entire training phase. This enables a one-shot
estimation of both the direct and cascaded channels and accommodates various
backscatter network configurations. We derive the conditions for optimal pilot
sequences and also establish that the minimum variance unbiased (MVU) estimator
attains the Cramer-Rao lower bound. Next, we propose new pilot designs to avoid
pilot contamination. We then present several linear estimation methods,
including least square (LS), scaled LS, and linear minimum mean square error
(MMSE), to evaluate the performance of our proposed scheme. We also derive the
analytical MMSE estimator using our proposed pilot designs. Furthermore, we
adapt our method for cellular-based passive Internet-of-Things (IoT) networks
with multiple tags and cellular users. Extensive numerical results and
simulations are provided to validate the effectiveness of our approach.
Notably, at least 10 dBm and 12 dBm power savings compared to the prior art are
achieved when estimating the direct and cascaded channels. These findings
underscore the practical benefits and superiority of our proposed technique
Enhancing AmBC Systems with Deep Learning for Joint Channel Estimation and Signal Detection
The era of ubiquitous, affordable wireless connectivity has opened doors to
countless practical applications. In this context, ambient backscatter
communication (AmBC) stands out, utilizing passive tags to establish
connections with readers by harnessing reflected ambient radio frequency (RF)
signals. However, conventional data detectors face limitations due to their
inadequate knowledge of channel and RF-source parameters. To address this
challenge, we propose an innovative approach using a deep neural network (DNN)
for channel state estimation (CSI) and signal detection within AmBC systems.
Unlike traditional methods that separate CSI estimation and data detection, our
approach leverages a DNN to implicitly estimate CSI and simultaneously detect
data. The DNN model, trained offline using simulated data derived from channel
statistics, excels in online data recovery, ensuring robust performance in
practical scenarios. Comprehensive evaluations validate the superiority of our
proposed DNN method over traditional detectors, particularly in terms of bit
error rate (BER). In high signal-to-noise ratio (SNR) conditions, our method
exhibits an impressive approximately 20% improvement in BER performance
compared to the maximum likelihood (ML) approach. These results underscore the
effectiveness of our developed approach for AmBC channel estimation and signal
detection. In summary, our method outperforms traditional detectors, bolstering
the reliability and efficiency of AmBC systems, even in challenging channel
conditions.Comment: Accepted for publication in the IEEE Transactions on Communication
Improved Signal Detection for Ambient Backscatter Communications
In ambient backscatter communication (AmBC) systems, passive tags connect to
a reader by reflecting an ambient radio frequency (RF) signal. However, the
reader may not know the channel states and RF source parameters and can
experience interference. The traditional energy detector (TED) appears to be an
ideal solution. However, it performs poorly under these conditions. To address
this, we propose two new detectors: (1) A joint correlation-energy detector
(JCED) based on the first-order correlation of the received samples and (2) An
improved energy detector (IED) based on the p-th norm of the received signal
vector. We compare the performance of the IED and TED under generalized noise
modeled using the McLeish distribution and derive a general analytical formula
for the area under the receiver operating characteristic (ROC) curves. Based on
our results, both detectors outperform TED. For example, the probability of
detection with a false alarm rate of 1% for JCED and IED is 14% and 5% higher,
respectively, compared to TED. These gains are even higher using the direct
interference cancellation (DIC) technique, with increases of 16% and 7%,
respectively. Overall, our proposed detectors offer better performance than the
TED, making them useful tools for improving AmBC system performance.Comment: This paper has got Major Revision by IEEE TGC
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
REITS: Reflective Surface for Intelligent Transportation Systems
Autonomous vehicles are predicted to dominate the transportation industry in
the foreseeable future. Safety is one of the major challenges to the early
deployment of self-driving systems. To ensure safety, self-driving vehicles
must sense and detect humans, other vehicles, and road infrastructure
accurately, robustly, and timely. However, existing sensing techniques used by
self-driving vehicles may not be absolutely reliable. In this paper, we design
REITS, a system to improve the reliability of RF-based sensing modules for
autonomous vehicles. We conduct theoretical analysis on possible failures of
existing RF-based sensing systems. Based on the analysis, REITS adopts a
multi-antenna design, which enables constructive blind beamforming to return an
enhanced radar signal in the incident direction. REITS can also let the
existing radar system sense identification information by switching between
constructive beamforming state and destructive beamforming state. Preliminary
results show that REITS improves the detection distance of a self-driving car
radar by a factor of 3.63
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