852 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
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
Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things
Backscatter communication (BC) technology offers sustainable solutions for
next-generation Internet-of-Things (IoT) networks, where devices can transmit
data by reflecting and adjusting incident radio frequency signals. In parallel
to BC, deep reinforcement learning (DRL) has recently emerged as a promising
tool to augment intelligence and optimize low-powered IoT devices. This article
commences by elucidating the foundational principles underpinning BC systems,
subsequently delving into the diverse array of DRL techniques and their
respective practical implementations. Subsequently, it investigates potential
domains and presents recent advancements in the realm of DRL-BC systems. A use
case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is
meticulously examined to highlight its potential. Lastly, this study identifies
and investigates salient challenges and proffers prospective avenues for future
research endeavors.Comment: 7,
6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap
The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communicatio
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN
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
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
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