1,515 research outputs found
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
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
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