Ambient Backscatter Communication Research Articles

Ambient Backscatter Communication With HARQ Assisted Hybrid Long-Short Packets

Ambient Backscatter Communication With HARQ Assisted Hybrid Long-Short Packets

Improved Energy-Based Signal Detection for Ambient Backscatter Communications

Improved Energy-Based Signal Detection for Ambient Backscatter Communications

Joint Localization and Signal Detection for Ambient Backscatter Communication Systems

Joint Localization and Signal Detection for Ambient Backscatter Communication Systems

Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network

Integrated Cognitive Symbiotic Computing and Ambient Backscatter Communication Network

QoS-Guaranteed Adaptive Power Reflection Coefficient for Self-Powered Cooperative Ambient Backscatter Communication

QoS-Guaranteed Adaptive Power Reflection Coefficient for Self-Powered Cooperative Ambient Backscatter Communication

Countering Eavesdroppers With Meta- Learning-Based Cooperative Ambient Backscatter Communications

Countering Eavesdroppers With Meta- Learning-Based Cooperative Ambient Backscatter Communications

Channel Estimation for Full-Duplex OFDM-Based Ambient Backscatter Communication Systems With I/Q Imbalance

Channel Estimation for Full-Duplex OFDM-Based Ambient Backscatter Communication Systems With I/Q Imbalance

Relay Assisted Cooperative Ambient Backscatter Communication With Hybrid Long-Short Packets

Relay Assisted Cooperative Ambient Backscatter Communication With Hybrid Long-Short Packets

Security-reliability trade-off analysis for transmit antenna selection in cognitive ambient backscatter communications

Massive deployment of IoT devices raises the need for energy-efficient spectrum-efficient low-cost communications. Ambient backscatter communication (AmBC) technology provides a promising solution to achieve that. Moreover, incorporating AmBC with cognitive radio networks (CRNs) achieves better spectrum efficiency; however, this comes with performance drawbacks. In this work, we investigate the security and reliability performance of an underlay CRN with AmBC, where the backscattering device (BD) exploits the radio frequency (RF) signals of the secondary transmitter (ST), and both the ST and the BD share a common receiver. Different from previous work, we consider an ST with multiple antenna. The ST employs a transmit antenna selection (TAS) scheme to enhance the ST performance and overcome the performance degradation caused by the BD interference. TAS exploits multiple antenna diversity with lower hardware complexity and power consumption. Considering the Nakagami-m fading model, closed-form expressions are derived for the outage probability (OP) and intercept probability (IP) of both the ST and the BD transmissions at the legitimate receiver and the eavesdropper. Moreover, the asymptotic behavior of OPs and IPs is also investigated in the high signal-to-noise ratio regime and the high main-to-eavesdropper ratio regime, respectively. Monte Carlo simulations are performed to validate the derived closed-form expressions. Numerical results show that employing TAS enhances the ST and BD reliability performance by percentages up to 98% and 80%, respectively, at high primary user interference threshold values. Moreover, it results in a better security-reliability trade-off for the ST and the BD.

Analysis of symbiotic backscatter empowered wireless sensors network with short-packet communications.

Recent progress studies in light of wireless communication systems mainly centred around two focuses: zero-energy consumption and ultra-reliable and low-latency communication (URLLC). Among various cutting-edge areas, exploiting ambient backscatter communication (Backcom) has recently been devised as one of the foremost solutions for achieving zero energy consumption through the viability of ambient radio frequency. Meanwhile, using short-packet communication (SPC) is the cheapest way to reach the goal of URLLCs. Upon these benefits, we investigate the feasibility of Backcom and SPC for symbiotic wireless sensor networks by analyzing the system performance. Specifically, we provide a highly approximated mathematical framework for evaluating the block-error rate (BLER) performance, followed by some useful asymptotic results. These results provide insights into the level of diversity and coding gain, as well as how packet design impacts BLER performance. Numerical results confirm the efficacy of the developed framework and the correctness of key insights gleaned from the asymptotic analyses.

Performance analysis of CDRT based cognitive NOMA vehicular networks with ambient backscatter relays

In order to improve the increasing spectrum requirements and the unguaranteed quality of service (QoS) for edge users in the internet of vehicles (IoV), many techniques such as cognitive radio (CR), non-orthogonal multiple access (NOMA) and coordinated direct and relay transmission (CDRT) have been proposed. To further enhance the spectrum utilization, this paper combines the above techniques with ambient backscatter communications (AmBC), which replaces the traditional relay in the network with a backscatter device (BD). When BD meets certain energy constraints (EC), an optimal dynamic reflect coefficient (RC) is enabled to promote the reflection effect. In this work, firstly, establish a CDRT-based CR-NOMA vehicular network with an ambient backscatter relay. Secondly, the closed expression for the outage probability of each secondary user is obtained with the help of Gauss Laguerre quadrature and Gauss Chebyshev quadrature. Then the system throughput analysis and the asymptotic analysis of each user when the maximum transmitting power as well as the interference temperature constrains (ITC) tends to infinity are performed. Finally, we performed simulations to reveal the impact of AmBC and ITC on edge users. Comparisons are also made with the system using fixed RC, the conventional system and its orthogonal multiple access (OMA) counterpart, demonstrating the superiority of our proposed system in improving the performance of edge users.

Adaptive Detection for Multiantenna Ambient Backscatter Communications System With MFSK Modulation

Adaptive Detection for Multiantenna Ambient Backscatter Communications System With MFSK Modulation

On the Unique Channel Identification in Ambient Backscatter Communication Systems

Channel coefficient estimation is a challenging problem in ambient backscatter communication systems. In this paper, unique identification of channel coefficients using the Expectation-Maximization (EM) method is investigated. It is proven that the EM method is not able to always yield unique solutions to channel coefficients and therefore the tag symbol estimation accuracy can be compromised. A correct derivation of the hybrid Cramer-Rao Bound (HCRB) is also provided.

Covert Ambient Backscatter Communication Against Randomly Distributed Wardens

Covert Ambient Backscatter Communication Against Randomly Distributed Wardens

Optimisation of energy efficiency of ambient backscatter communication and reconfigurable intelligent surfaces in non‐orthogonal multiple access downlink

AbstractThe authors study the energy efficiency (EE) of ambient backscatter communication (AmBC) device‐assisted and reconfigurable intelligent surfaces (RIS)‐assisted non‐orthogonal multiple access (NOMA) downlinks. The authors establish two optimisation problems based on the two collaborative devices (AmBC devices, RIS) with the objective of maximising the EE of the system, taking into account the requirements of power limitation and rate limitation, etc. and also obtain the solutions of two problems by optimising the relevant performance metrics based on the alternating optimisation algorithm. For the backscatter device (BD)‐aided downlink NOMA network, the problem is first decoupled into three subproblems, where the power allocation optimisation subproblem is solved by using the quadratic transformation method and the subgradient algorithm. The maximum EE is obtained by iterating according to the Dinkelbach's algorithm. For the RIS‐aided downlink NOMA network, the power allocation problem is solved by the same method as above and the phase optimisation problem is solved by the successive convex approximation method. Numerical results show that the proposed algorithm can achieve convergence after several iterations, and the EE of systems with BD‐assisted and RIS‐assisted have different levels of sensitivity to different influencing factors.

Performance Analysis for Ambient Backscatter Communications With Hybrid Long-Short Packets

Performance Analysis for Ambient Backscatter Communications With Hybrid Long-Short Packets

Sum rate maximization for RIS-assisted NOMA-enabled underlay AmBC-CR networks

Ambient backscatter communications (AmBC) have introduced a revolutionary approach to wireless communication that utilizes ambient radio frequency (RF) signals in contrast to self-generated RF signals. This makes AmBC a compelling candidate for future Internet-of-Things (IoT) applications, particularly considering the constraints of energy and spectrum resources. In this work, we investigate a cognitive radio (CR) based underlay network of backscatter devices (BDs) that communicate with the backscatter receiver (BR) by utilizing non-orthogonal multiple access (NOMA). Due to the dual fading effect in backscatter networks, the backscatter link is weakened which degrades the achievable performance. In order to tackle this issue, reconfigurable intelligent surface (RIS) is exploited in such networks. Therefore, to maximize the sum rate of the RIS-assisted NOMA-enabled underlay AmBC-CR (RIS-NUAC) network, subject to individual BDs’ data rate constraint and primary receiver (PR) interference threshold constraint, we jointly optimize the phase shift matrix (PSM) at the RIS and the power reflection coefficients (PRCs) at the BDs. To solve the resultant non-convex optimization problem, we propose a sub-optimal solution based on an alternating optimization (AO) algorithm by applying semidefinite relaxation (SDR) and rank one approximation. Based on simulation results, the proposed scheme demonstrates superior performance over baseline schemes, including NOMA with random phases, NOMA without RIS, orthogonal multiple access (OMA) with optimal phases, and OMA without RIS.

Code Index Modulated Chaotic Ambient Backscatter Communication With Differential Walsh Codes

Code Index Modulated Chaotic Ambient Backscatter Communication With Differential Walsh Codes

Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network

Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.

Machine learning classifiers for signal detection in ambient backscatter communication under time-selective fading

In the world of the Internet of Things (IoT), ambient backscatter communication, or AmBaC, is a cutting-edge technology that has attracted a lot of interest. It meets the requirement for low-power communication in IoT devices, which are often located in a multitude of settings and scenarios where power efficiency is essential. In the literature, most signal detection is carried out using an energy detector or Minimum Mean Square Error (MMSE) detector under static conditions. In this work, we perform signal detection using multiple supervised machine learning (ML) classifiers, namely random forest, K-nearest neighbors (KNN), and support vector machine (SVM), and we examine the system performance in terms of bit error rate (BER) for both stationary and moving radio-frequency (RF) source. The simulation findings demonstrate that the proposed supervised ML classifiers surpass the conventional signal detector. In particular, the performance of the SVM classifier is best among all the other ML classifiers. Additionally, we examined the performance of different kernels of the SVM classifier and discovered that the Gaussian kernel offers the highest performance when it comes to SVM classifier performance.