Ambient Backscatter Communication Systems Research Articles

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.

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

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets

Adaptive Detection for Multi-Antenna Ambient Backscatter Communications System With MFSK Modulation

Adaptive Detection for Multi-Antenna Ambient Backscatter Communications System With MFSK Modulation

Dual-Polarization Ambient Backscatter Communications and Signal Detection.

Ambient backscatter communication (AmBC), an emerging mechanism for batteryless communications that can utilize ambient radio-frequency signals to modulate information and thus reduce power consumption, has attracted considerable attention and has been considered as a critical technology in green "Internet of Things" sensor networks due to its ultra-low power consumption. This paper presents the first a complete dual-polarization AmBC (DPAm) system model, which can extend AmBC into polarization diversity and improve the data-transmission rate of backscatter symbols. We proposed two scenarios: direct dual-polarization-based DPAm node structures and polarization-conversion-based DPAm node structures. In addition, we consider a parallel backscatter mode with differential coding and develop corresponding detectors, which also give the analytical detection thresholds. Moreover, we consider a simultaneous backscatter mode with Manchester coding in order to avoid complex-parameter estimation. To address the power imbalance problem of the DPAm system that arises because the polarization deflection angle would cause the power level to change with different polarization patterns, we also develop a power-average detector and a clustering detector. Simulation results show the throughput performance on each DPAm node structure with each detector, demonstrating the feasibility and efficiency of the proposed DPAm nodes and detectors. Compared with single-polarization AmBC (SPAm), the proposed DPAm node can achieve higher throughput in most cases. Finally, the clustering detector is shown to be more robust to short training sequences and complex environments.

Two Distributed IRSs-Assisted Ambient Backscatter Communication System

Two Distributed IRSs-Assisted Ambient Backscatter Communication System

Relay Selection for Dual-Hop Cooperative Ambient Backscatter Communication Systems.

Previous works only focus on the optimization design for the dual-hop cooperative ambient backscatter communication (AmBC) system with single-relay selection. The impact of relay selection on the outage performance of dual-hop cooperative AmBC systems is still missing. Motivated by this, in this paper, we investigate the outage performance of a dual-hop cooperative AmBC system with single-relay selection, where the backscatter link shares the receiver with the cellular link and the harmful direct-link interference (DLI) is mitigated by using successive interference cancellation (SIC). In the system considered, the selected relay has dual functions. One is to forward message for the cellular link, and the other is to act as the radio-frequency (RF) source for the backscatter device (BD). Specifically, after proposing two novel single-relay selection schemes (RSSs), namely reactive RSS and proactive RSS, we derive the closed-form outage probability (OP) expressions for both RSSs, which can be performed in a distributed manner. To gain more insights, the asymptotic OPs at high signal-to-noise ratio (SNR) are explored and the outage performance comparison between the reactive RSS and proactive RSS are also provided. Results show that the proposed reactive RSS is outage-optimal among all possible single-relay selection schemes. The theoretical analysis is validated by Monte Carlo simulations. The results also show that the relay selection scheme, the number of relays, the location of BD, and the reflection coefficient of BD have great impact on the outage performance of cooperative AmBC systems.

RIS-Assisted Ambient Backscatter Communication for SAGIN IoT

The space, air and ground integrated network (SAGIN) will greatly promote the development of the Internet of Things (IoT). Green IoT will be an important part of SAGIN. Ambient backscatter communication (AmBC) is a potential solution for green SAGIN IoT. To improve the achievable sum rate (ASR) of the AmBC system, we propose a reconfigurable intelligent surface (RIS) assisted AmBC system. In the single-BD AmBC scenario, we firstly give the phase shifts that maximize the gain of the reflection link of the AmBC system, and then give the optimal reflection coefficient. The proposed scheme does not need to solve the convex semidefinite program (SDP) problem, and has the characteristics of low computational complexity. In the multi-BD AmBC scenario, we first propose a multi-BD phase shifts initialization strategy to ensure the stability of the proposed scheme. Then, we give the optimal reflection coefficient and phase shifts based on iterative method. Simulations show that the RIS-assisted AmBC scheme is superior to the non-RIS-assisted AmBC scheme.

Channel Estimation for Full-Duplex Multi-Antenna Ambient Backscatter Communication Systems

Ambient backscatter communication (AmBC) is a highly promising technology that enables the ubiquitous deployment of low-cost, low-power devices to support the next generation of Internet-of-Things (IoT) applications. This paper addresses channel estimation for full-duplex multi-antenna AmBC systems. This is highly challenging due to the large number of channel parameters resulting from the use of multiple antennas, the presence of self-interference, and the dependence of the backscattering channel on the state of the backscattering device. Considering both pilot-based and semi-blind estimation strategies, we propose three solutions for this problem. The first is the pilot-based maximum-likelihood (ML) estimator. The second is a semi-blind estimator based on the expectation maximization (EM) framework, which provides higher accuracy than the ML, at the cost of higher computational complexity. The third is a semi-blind estimator based on the decision-directed (DD) strategy, which provides a tradeoff between the ML and the EM. Additionally, we derive the exact Cramer-Rao bound (CRB) for pilot-based estimation and the modified CRB for semi-blind estimation. Simulations show that the ML and the EM perform very close to their respective CRBs, and that the semi-blind estimators offer significantly higher estimation accuracy, as well as superior symbol-error-rate performance, compared to the ML estimator.

Deep Unfolding-Based Joint Beamforming and Detection Design for Ambient Backscatter Communications With IRS

In this letter, we investigate a novel ambient backscatter communication (AmBC) system in which an intelligent reflecting surface (IRS) acts as a passive transmitter to communicate with a reader. We are interested in the joint design of the IRS reflecting beamforming and symbol detection to minimize the bit error rate (BER). However, the problem is challenging to be solved optimally, since the BER is related to the clustering-based detector without a concrete close-form expression and the IRS reflecting unit modulus constraint is non-convex. To solve this issue, we propose a novel deep unfolding neural network (DUNN) combining data-driven and model-driven for passive reflecting beamforming design and symbol detection, which is learned to approximate the BER model from the training samples and the unit modulus constraint is satisfied by treating the optimization variables as network parameters. Numerical results demonstrate that the proposed scheme has superior performance of detection.

Gaining From Multiple Ambient Sources: Signaling Matrix for Multi-Antenna Backscatter Devices

The backscatter device (BD) in ambient backscatter communication (AmBC) systems is often illuminated by multiple ambient sources in reality. Although multiple ambient sources can benefit the AmBC systems, this scenario has been rarely studied. This letter gives an answer to the overlooked question of how to effectively obtain the gain from multiple ambient sources—using a proper signaling matrix on a multi-antenna BD. We optimize the BD signaling matrix based on the criterion of minimizing the AmBC system bit-error-rate (BER) performance. The derived signaling matrices combine the ambient signals and steer the backscatter signal toward the receiver to increase the received signal strength. The simulation results show that the multi-antenna BD effectively uses multiple ambient sources, and the derived BD signaling matrices robustly achieve a larger communication range or a higher datarate of AmBC systems.

Optimum Multiantenna Ambient Backscatter Receiver for Binary-Modulated Tag Signals

Ambient backscatter communication (AmBC) is becoming increasingly popular as a green Internet-of-things technology by enabling ultra-low-power data exchanges among simple tags. The bit-error-rate (BER) performance of the AmBC receivers is hampered by the low signal-to-interference-plus-noise ratio (SINR), which limits either the range or the data rate that can be supported by these systems. Among the alternatives, the solutions provided by a multiantenna receiver enable several practical methods to improve the SINR using array signal processing. In this paper, the optimum multiantenna AmBC receiver for any binary modulated tag signal is presented. Two receivers are derived from the maximum-a-posterior probability criterion for deterministic-unknown and for Gaussian distributed ambient signals. The closed-form cumulative distribution functions are derived for both of the receivers for performance evaluation purposes. It is discussed that these two receivers are equivalent under practical conditions, and the optimum receiver is a generalization of the multiantenna receivers available in the literature. Several implementation details are discussed, and numerical evaluation is provided to validate the development. Therefore, the presented receiver accommodates different tag modulations and achieves the best possible BER performance, which opens up new application possibilities by improving AmBC system flexibility.

Security Analysis of Triangle Channel-Based Physical Layer Key Generation in Wireless Backscatter Communications

Ambient backscatter communication (AmBC) enables ultra-low-power communications by backscattering ambient radio frequency (RF) signals and harvesting energy simultaneously. It has emerged as a cutting-edge technology for supporting a variety of Internet of Things (IoT) applications. However, existing research lacks effective secret key sharing schemes for safeguarding communications between resource-constrained backscatter devices (BDs) in AmBC systems. In this paper, we present, Tri-Channel, a novel physical layer key generation scheme between two BDs by multiplying downlink signals and backscatter signals to obtain the information of a triangle channel as a shared random secret source for key generation. In particular, we analyze the security of our scheme under both passive and active attacks, concretely Eavesdropping Attack (EA), Control Channel Attack (CCA), Signal Manipulative Attack (SMA), and Untrusted RF-Source Attack (URSA). Through theoretical analysis and simulations by comparing with a traditional scheme (named Tradi-Channel), we found that our scheme consistently outperforms the Tradi-Channel under the EA and two active attacks (CCA and SMA). In addition, it shows better security performance under URSA, which is proposed based on the unauthenticated characteristic of BDs in Tri-Channel, even though URSA is more vital than SMA. Concretely, Tri-Channel’s secret key rate (SKR) outperforms Tradi-Channel’s under the above four passive and active attacks. This implies that our scheme is advanced in terms of both security and efficiency of key generation. Numerous extensive simulations further prove our theoretical analysis results.

Effect of Dynamic Reflection Coefficient on Backscatter System in Time-Selective Scenario

In the performance evaluation of the ambient backscatter communication (AmBC) system the energy harvesting (EH) by the passive tags has been mainly overlooked. This letter investigates the impact of EH on the multi-tag AmBC system by considering non-linear EH along with time-selective fading conditions. Specifically, we derived a closed-form expression for outage probability and provide a comparative outage analysis between dynamic reflection coefficient (RC) and fixed RC. To gain additional insights, we also explored the asymptotic outage probability and diversity order when the signal to noise ratio (SNR) is very high. Our findings indicate that the use of dynamic RC decreases the outage probability as compared to fixed RC. Additionally, mobility of the reader reduces outage performance albeit with dynamic RC consideration.

RIS-Empowered Ambient Backscatter Communication Systems

Passive tags must harvest sufficient energy to operate. However, ensuring the tag’s energy needs and simultaneously suppressing the interference in ambient backscatter systems is challenging. To meet this, we employ a reconfigurable intelligent surface (RIS) to amplify the ambient source signal that reaches the tag. We maximize the achievable rate by optimizing the RIS phase-shifts subject to the tag’s energy harvesting constraint. Specifically, the phase-shifts are optimized to increase the tag’s received power while suppressing the direct-and-RIS-cascaded link interference. The optimization problem is non-convex, and we convexify it by means of fractional programming and a quadratic transformation. In comparison to a benchmark, our optimal phase-shift design provides ~150% and ~250% gains in harvested power and achieved rate, respectively, at 20 dB m transmit power.

Joint Resource Allocation for Full-Duplex Ambient Backscatter Communication: A Difference Convex Algorithm

Nowadays, Ambient Backscatter Communication (AmBC) systems have emerged as a green communication technology to enable massive self-sustainable wireless networks by leveraging Radio Frequency (RF) Energy Harvesting (EH) capability. A Full-duplex Ambient Backscatter Communication (FAmBC) network with a Full-duplex Access Point (AP), a dedicated Legacy User (LU), and several Backscatter Devices (BDs) is considered in this study. The AP with two antennas transfers downlink Orthogonal Frequency Division Multiplexing (OFDM) information and energy to the dedicated LU and several BDs, respectively, while receiving uplink backscattered information from BDs at the same time. One of the key aims in AmBC networks is to ensure fairness among BDs. To address this, we propose the Multi-objective Lexicographical Optimization Problem (MLOP), which aims to maximize the minimum BD’s throughput while enhancing overall BDs’ throughput, subject to the AP’s subcarrier power, BDs’ reflection coefficients, and backscatter time allocation. Owe to the MLOP is non-convex, we propose Difference Convex Algorithm (DCA) using Exterior Penalty Function Method (EPFM)—an inventive non-convex optimization method— to reach the optimal solution. The most critical advantage of applying this proposed approach is finding the globally optimal solution. The effectiveness of the proposed method supported by theoretical analysis confirms its superiority compared to some of the investigated suboptimal algorithms with the same computational complexity.

RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning.

Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously. Secondly, a smart twin delayed deep deterministic (TD3) AmBC signal detection method is presented, based on deep reinforcement learning. Extensive quantitative and qualitative experiments are performed, which show that the proposed method is more compelling than the outstanding comparison methods.

Azimuth Estimation for the Backscatter Device in Ambient Backscatter Communication System

In this letter, we study the azimuth estimation for the backscatter device (BD) in ambient backscatter communication system, where the receiver is equipped with the uniform linear array (ULA). By utilizing the covariance matrices in two symbol periods with different BD symbols, we apply the conventional multiple signal classification (MUSIC) algorithm to estimate the azimuths of both the BD and the ambient radio frequency (RF) source. Then, we propose a novel eigenvector-based algorithm to identify the azimuth of the BD from the two estimated azimuths. Moreover, we qualitatively analyze the impacts of the reflection coefficients on the performance of the proposed azimuth estimation approach. Finally, the simulation results are provided to validate the effectiveness of the proposed method and to justify the qualitative analytical conclusions.

Generalized Space Shift Keying for Ambient Backscatter Communication

We consider a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. We propose a scheme to exploit the multiple antenna structure of the system to achieve a lower error-rate performance than conventional ABC systems. Furthermore, we present a novel low complexity energy-based maximum likelihood (EML) GSSK detector, which does not require the perfect knowledge of the ambient source’s signal, unlike the conventional ABC receivers. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression as the number of antennas of the reader becomes large. Finally, we derive a simple, asymptotic PEP at the reader when the noise variance approaches zero, i.e., under the large signal-to-interference ratio regime. We validate our analysis through Monte Carlo simulations and show a small performance loss due to the approximations.

Energy Efficient Transmission Design for NOMA Backscatter-Aided UAV Networks with Imperfect CSI

The recent combination of ambient backscatter communication (ABC) with non-orthogonal multiple access (NOMA) has shown great potential for connecting large-scale Internet of Things (IoT) in future unmanned aerial vehicle (UAV) networks. The basic idea of ABC is to provide battery-free transmission by harvesting the energy of existing RF signals of WiFi, TV towers, and cellular base stations/UAV. ABC uses smart sensor tags to modulate and reflect data among wireless devices. On the other side, NOMA makes possible the communication of more than one IoT on the same frequency. In this work, we provide an energy efficient transmission design ABC-aided UAV network using NOMA. This work aims to optimize the power consumption of a UAV system while ensuring the minimum data rate of IoT. Specifically, the transmit power of UAVs and the reflection coefficient of the ABC system are simultaneously optimized under the assumption of imperfect channel state information (CSI). Due to co-channel interference among UAVs, imperfect CSI, and NOMA interference, the joint optimization problem is formulated as non-convex, which involves high complexity and makes it hard to obtain the optimal solution. Thus, it is first transformed and then solved by a sub-gradient method with low complexity. In addition, a conventional NOMA UAV framework is also studied for comparison without involving ABC. Numerical results demonstrate the benefits of using ABC in a NOMA UAV network compared to the conventional UAV framework.