2,638 research outputs found
Signal Detection in Ambient Backscatter Systems: Fundamentals, Methods, and Trends
Internet-of-Things (IoT) is rapidly growing in wireless technology, aiming to
connect vast numbers of devices to gather and distribute vital information.
Despite individual devices having low energy consumption, the cumulative demand
results in significant energy usage. Consequently, the concept of
ultra-low-power tags gains appeal. Such tags communicate by reflecting rather
than generating the radio frequency (RF) signals by themselves. Thus, these
backscatter tags can be low-cost and battery-free. The RF signals can be
ambient sources such as wireless-fidelity (Wi-Fi), cellular, or television (TV)
signals, or the system can generate them externally. Backscatter channel
characteristics are different from conventional point-to-point or cooperative
relay channels. These systems are also affected by a strong interference link
between the RF source and the tag besides the direct and backscattering links,
making signal detection challenging. This paper provides an overview of the
fundamentals, challenges, and ongoing research in signal detection for AmBC
networks. It delves into various detection methods, discussing their advantages
and drawbacks. The paper's emphasis on signal detection sets it apart and
positions it as a valuable resource for IoT and wireless communication
professionals and researchers.Comment: Accepted for publication in the IEEE Acces
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
RIScatter: unifying backscatter communication and reconfigurable intelligent surface
Backscatter Communication (BackCom) nodes harvest energy from and modulate information over an external electromagnetic wave. Reconfigurable Intelligent Surface (RIS) adapts its phase shift response to enhance or attenuate channel strength in specific directions. In this paper, we show how those two seemingly different technologies (and their derivatives) can be unified to leverage their benefits simultaneously into a single architecture called RIScatter. RIScatter consists of multiple dispersed or co-located scatter nodes, whose reflection states can be adapted to partially engineer the wireless channel of the existing link and partially modulate their own information onto the scattered wave. This contrasts with BackCom (resp. RIS) where the reflection pattern is exclusively a function of the information symbol (resp. Channel State Information (CSI)). The key principle in RIScatter is to render the probability distribution of reflection states (i.e., backscatter channel input) as a joint function of the information source, CSI, and Quality of Service (QoS) of the coexisting active primary and passive backscatter links. This enables RIScatter to softly bridge, generalize, and outperform BackCom and RIS; boil down to either under specific input distribution; or evolve in a mixed form for heterogeneous traffic control and universal hardware design. For a single-user multi-node RIScatter network, we characterize the achievable primary-(total-)backscatter rate region by optimizing the input distribution at the nodes, the active beamforming at the Access Point (AP), and the backscatter detection regions at the user. Simulation results demonstrate RIScatter nodes can exploit the additional propagation paths to smoothly transition between backscatter modulation and passive beamforming
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