Backscatter Communication Networks Research Articles

Full-Duplex-Enhanced Wireless-Powered Backscatter Communication Networks: Radio Resource Allocation and Beamforming Joint Optimization

Full-Duplex-Enhanced Wireless-Powered Backscatter Communication Networks: Radio Resource Allocation and Beamforming Joint Optimization

Node clustering scheme supporting mobility in NOMA-enabled backscatter communication networks

In this paper, we study the problem of mobile node clustering in non-orthogonal multiple access (NOMA) backscatter communication networks. The nodes are clustered based on the differences between their channel gains to accomplish NOMA transmission within the clusters. However, as the channel gains change constantly with node mobility, the nodes need to be re-clustered, which leads to a significant computational overhead. We propose a node clustering scheme (IGAC). This scheme formulates the node clustering problem as a combinatorial optimization problem to find the optimal node clustering that maximizes system throughput. To handle the large search space of multi-node clustering, we employ a genetic algorithm to solve this optimization problem effectively. Moreover, to overcome the NOMA principle violation problem(NPVP) caused by mobile nodes, we propose a node clustering adjustment scheme (SMSO) based on stable matching ideas and swapping operations. According to the theory of stable matching, optimizing the long-term throughput of the system can be achieved by swapping nodes between different clusters to eliminate blocking pairs in clustering. The simulation results show that our proposed node clustering scheme outperforms traditional schemes in terms of system throughput.

Throughput Maximization for NOMA-Based Cognitive Backscatter Communication Networks With Imperfect CSI

Throughput Maximization for NOMA-Based Cognitive Backscatter Communication Networks With Imperfect CSI

Maximizing Effective Energy Efficiency for Wireless-Powered BackCom Networks

In wireless-powered backscatter communication (WP-BackCom) networks, backscatter transmitters modulate their information on the radio frequency (RF) signals received from the power beacon (PB) and reflect the modulated RF signal to their intended receivers. To optimize delay-quality of service provisioning and network power consumption, this letter proposes an interference-aware resource allocation scheme that aims to maximize effective energy efficiency (EEE) for WP-BackCom networks. In this scheme, reflection coefficients are analytically obtained and power allocation is performed at the PB via convex optimization. Results show that the proposed scheme substantially outperforms the baseline schemes in terms of EEE.

UAV-Powered Multi-User Intelligent Reflecting Surface Backscatter Communication

This paper investigates unmanned aerial vehicle (UAV) powered multi-user intelligent reflecting surface (IRS) backscatter communication (BackCom). In such a system, an IRS as a passive transmitter sends multiple data streams towards multiple users in a manner of BackCom, while a UAV serves as a mobile power beacon for flexibly supplying radio frequency signals. To maximize the sum rate of all the users, the trajectory of the UAV and its transmit beamforming, as well as the passive beamforming at the IRS, are optimized jointly. Specifically, a block coordinate descent method is firstly leveraged to decompose the original non-convex optimization problem into three tractable subproblems. By developing an alternate iterative algorithm, the transmit beamforming at the UAV, the passive beamforming at the IRS, and the trajectory of the UAV are circularly optimized by employing fractional programming, semidefinite programming, reinforcement learning, etc. The simulation results demonstrate that the proposed joint optimization scheme is feasible in the proposed IRS backscatter communication network.

Resource allocation of backscatter communication based on reconfigurable intelligent surface

AbstractIn this article, we propose a double reconfigurable intelligent surface (RIS)‐aided ambient backscatter communication (AmBC) network combined with non‐orthogonal multiple access (NOMA) technology, which not only successfully assists the base station (BS) in connecting with the cell edge user, but also enhances backscatter communication rate, energy efficiency, and spectrum efficiency. In an effort to obtain the highest backscatter communication rate, we design a RIS and BS‐based resource allocation problem which is solved by determining the optimal phase shifts of RISs and the optimal power allocation for signals of NOMA users. We break it down into two smaller challenges in order to discover the best solution. For one thing, we iterate the optimal phase shifts between the two RIS combinations using an alternating optimization technique. For another, the bisection method is used to provide the NOMA user with the ideal power allocation. Finally, the results of the simulation substantiate the viability of the proposed scheme.

Joint Optimization of Trajectory and Discrete Reflection Coefficients for UAV-Aided Backscatter Communication System with NOMA

Backscatter communication is a promising technology for the Internet of Things (IoT) systems with low-energy consumption, in which the data transmission of the backscatter devices relies on reflecting the incident signal. However, limited by the low power characteristic of the reflected signal from backscatter devices, achieving efficient data collection for the widely distributed backscatter devices is a thorny problem. Considering that unmanned aerial vehicles (UAVs) have flexible deployment capability, employing UAVs in a backscatter communication network can achieve feasible data collection for backscatter devices. In this paper, we consider a UAV-aided backscatter system and introduce Non-orthogonal multiple access (NOMA) to enable the UAV to collect signals from multiple backscatter devices simultaneously. We formulate an optimization problem to maximize the communication throughput of the considered system by jointly designing the backscatter device matching, the trajectory of the UAV, and the reflection coefficients of the backscatter devices, which is a non-convex optimization problem and challenging to solve. Hence, we decouple the original problem into three sub-problems and propose an efficient iterative algorithm based on Block Coordinate Descent (BCD) to solve them. In detail, a game-based matching algorithm is designed to ensure the transmission needs of remote backscatter devices. The UAV trajectory and reflection coefficients of backscatter devices are optimized through the Successive Convex Approximation (SCA) algorithm and relaxation algorithm. By iterative optimization of the sub-problems, the original problem is solved. The simulation results show that the proposed scheme can obtain a significant throughput gain compared to benchmark schemes.

Sum Rate Optimization for Multiple Access in Multi-FD-UAV-Assisted NOMA-Enabled Backscatter Communication Network

With the rapid development of the Internet of Things (IoT) network, research on low-power and energy-saving devices has attracted extensive attention from both academia and the industry. Although the backscatter devices (BDs) that utilize the environmental power to activate circuits and transmit signals are a promising technology to be deployed as IoT nodes, it is challenging to design a flexible data backhaul scheme for massive BDs. Therefore, in this paper, we consider an unmanned-aerial-vehicle (UAV)-assisted backscatter communication network, where BDs are served by multiple full-duplex (FD) UAVs with the non-orthogonal multiple access (NOMA) schemes and modulate their signals on the downlink signals, which are generated by the UAVs to serve the coexisting regular user equipments (UEs). To maximize the sum rate of the considered system, we construct an optimization problem to optimize the reflection coefficient of BDs, the downlink and the backhaul transmission power, and the trajectory of UAVs jointly. Since the formulated problem is a non-convex optimization problem and is difficult to solve directly, we decouple the original problem into three sub-problems and solve them with the successive convex approximation (SCA) method, thereby addressing the original problem by a block coordinate descent (BCD)-based iterative algorithm. The simulation results show that, compared with the benchmark schemes, the proposed algorithm can obtain the highest system sum rate and utilize limited time-frequency resources more efficiently.

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.

Coding Techniques for Backscatter Communications—A Contemporary Survey

Backscatter communication (BackCom) networks enable passive/battery-free Internet-of-Thing devices, providing reliable, massive connectivity while ensuring self-sustainability, low maintenance, and low costs. Effective channel codes and decoding algorithms are necessary to achieve these objectives. However, a comprehensive survey/review paper on such techniques for BackCom networks has not been available. This paper aims to fill this gap. Because tags have limited computational resources, traditional coding techniques may not suit them. We first describe the basics of BackCom, channel codes and their relevant design parameters, and codes for general communication networks. We address the BackCom limitations, requirements, and channel characteristics. As conventional codes may not seamlessly move to the BackCom arena, we identify the potential BackCom coding techniques and multiple access schemes. We further highlight potential approaches for addressing code implementation complexity and reliability. Finally, we discuss open issues, challenges, and potential future research directions.

Secrecy Performance of Backscatter Communications With Multiple Self-Powered Tags

Backscatter communication (BackCom) networks are vulnerable to eavesdropping, while its secrecy performance has not been sufficiently studied. In this letter, we propose a tag selection strategy to optimize the ergodic secrecy capacity (ESC) and the secrecy outage probability (SOP) simultaneously for a multi-tag self-powered BackCom network in the presence of an eavesdropper. Concretely, an adaptive power reflection coefficient scheme is first designed to maximize the instantaneous secrecy capacity per tag, based on which the one with the highest secrecy capacity is selected to backscatter message. To evaluate the resulting secrecy performance, we first obtain the exact cumulative distribution function of the instantaneous secrecy capacity under independent but non-identically distributed Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channels, and then derive the ESC and the SOP, respectively. Simulation results validate the theoretical analyses and demonstrate that the proposed strategy is superior to the existing strategies.

Backscatter Sensors Communication for 6G Low-Powered NOMA-Enabled IoT Networks Under Imperfect SIC

The combination of nonorthogonal multiple access (NOMA) using power-domain with backscatter communication (BC) is expected to connect large-scale Internet of things (IoT) devices in the future sixth-generation era. This article introduces a BC in a multicell IoT network, where a source in each cell transmits a superimposed signal to its associated IoT devices using NOMA. The backscatter sensor tag (BST) also transmits data to IoT devices by reflecting and modulating the superimposed signal of the source. A new optimization framework is provided that simultaneously optimizes the total power of each source, power allocation coefficient of IoT devices, and RC of BST under imperfect successive interference cancellation decoding. This work aims to maximize the total energy efficiency (EE) of the IoT network subject to the quality of services of each IoT device. The problem is first transformed using the Dinkelbach method and then decoupled into two subproblems. The Karush–Kuhn–Tucker conditions and dual Lagrangian method are employed to obtain efficient solutions. In addition, we also calculate the EE of the conventional NOMA network without BC as a benchmark framework. Simulation results unveil the advantage of our considered NOMA BC network over the conventional NOMA network in terms of system total EE.

Resource allocation for UAV-assisted backscatter communication

As a promising new technology for green communication, backscatter communication has attracted wide attention in academics and industry. This paper studies the resource allocation problem for an unmanned aerial vehicle (UAV)-assisted backscatter communication network. The UAV acts as an airborne mobile base station and broadcasts signals to the ground backscatter devices (BDs), which transmit the data signals to the backscatter receiver (BR) in a backscattered manner. Specifically, the ground-based BDs communicate with the BR using a dynamic protocol based on time division multiple access. Considering the fairness among BDs, we aim to investigate maximizing the minimum (max–min) rate of the proposed network by jointly optimizing backscatter device scheduling, reflection coefficient, UAV’s power control, and UAV’s trajectory. The optimization problem is a non-convex problem, which is challenging to obtain the optimal solution. Therefore, we propose an efficient iterative algorithm to decompose the optimization problem into four subproblems by the block coordinate descent method. The variables are alternatively optimized by the interior point method and successive convex approximation techniques in each iteration. Finally, simulation results show that the max–min rate of the system obtained by the proposed scheme outperforms other benchmark schemes.

Performance on Cluster Backscatter Communication Networks With Coupled Interferences

This article presents an analytical model to analyze the communication performance of cluster backscatter communication networks (CBackCom Nets) by considering their unique interferences. In CBackCom Nets, interferences are from both backscatter transmitters (BTs) and carrier emitters (CEs), i.e., RF signal emitters. Because BTs are distributed in clusters around CEs, interferences from BTs and interferences from CEs constitute coupled interferences. In addition, since BTs conduct backscatter communications by reflecting RF signals from CEs, interfering signals from BTs, and interfering signals from CEs are power-correlated, leading to the particularity and complexity of coupled interferences of CBackCom Nets. In contrast to previous studies that analyze the performance of CBackCom Nets ignoring coupled interferences, this article develops a novel interference analysis approach to analyze their coupled interferences, and then analyze performance, including coverage probability and spatial throughput of a cluster. Our numerical results show that our analytical model can obtain more accurate results than prior analytical models. In addition, our results reveal the relationship between the communication performance and multiple factors, such as the node density, the energy harvesting model, the interferences from CEs, and the cluster size, offering insightful implications for constructing and configuring CBackCom Nets.

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.

Modeling and Performance Analysis of Large-Scale Backscatter Communication Networks with Directional Antennas.

Backscatter communication (BackCom) constitutes intriguing technology that enables low-power devices in transmitting signals by reflecting ambient radio frequency (RF) signals that consume ultra-low energy. Applying the BackCom technique in large-scale networks with massive low-power devices can effectively address the energy issue observed in low-power devices. Prior studies only consider large-scale BackCom networks equipped with omni-directional antennas, called Omn-BackCom Net. To improve the network’s performance, we employ directional antennas in large-scale BackCom networks, called Dir-BackCom Nets. This article establishes a theoretical model for analyzing the performance of Dir-BackCom Nets. The performance metrics include both connectivity and spatial throughput. Our model is genaralized for both Dir-BackCom Nets and Omn-BackCom Net. The accuracy of our theoretical model is verified by extensive simulations. Results indicate that Dir-BackCom Nets can improve connectivity and spatial throughput. Moreover, results show that the throughput can be maximized by choosing an optimal density of BTs. In addition, both the connectivity and spatial throughput of BackCom Nets can be improved by choosing a directional antenna with a proper beamwidth and gain of the main lobe. Our theoretical model and results can offer beneficial implications for constructing Dir-BackCom Nets.

Maximizing Secondary Users’ Sum-Throughput in an In-Band Full-Duplex Cognitive Wireless Powered Backscatter Communication Network

This article studies the secondary users’ sum-throughput in an in-band full-duplex (I-FD) cognitive wireless powered backscatter communication network. The secondary network consists of a hybrid access point (H-AP) and a finite number of geographically distributed secondary backscatter sensors (SBSs), each capable of using either the conventional or backscatter communication. The secondary network shares the primary network’s spectrum using an underlay spectrum sharing model. In this model, the H-AP and SBSs operate in I-FD mode to achieve improved spectral and time efficiency. Moreover, when an SBS has little available energy, it switches to the low-energy consumption backscatter method instead of the conventional transmission method. The goal is to maximize the sum-throughput of the SBSs. It is shown that such a problem is a convex optimization problem. Closed-form expressions for the optimal allocated time and energy to SBSs are derived and solved via a low complexity and efficient algorithm called joint optimal time and energy allocation (JOTEA). Numerical results illustrate that the JOTEA algorithm achieves a higher sum-throughput than the benchmark equal time allocation method. Furthermore, if the self-interference cancellation circuit considerably cancels self-interference in the H-AP, the I-FD mode achieves a higher sum-throughput performance than that of the half-duplex mode. Moreover, using backscatter communication results in a further increase in the sum-throughput.

EDChannel: channel prediction of backscatter communication network based on encoder-decoder

Backscatter communication networks have attracted much attention due to their small size and low power waste, but their spectrum resources are very limited and are often affected by link bursts. Channel prediction is a method to effectively utilize the spectrum resources and improve communication quality. Most channel prediction methods have failed to consider both spatial and frequency diversity. Meanwhile, there are still deficiencies in the existing channel detection methods in terms of overhead and hardware dependency. For the above reasons, we design a sequence-to-sequence channel prediction scheme. Our scheme is designed with three modules. The channel prediction module uses an encoder-decoder based deep learning model (EDChannel) to predict the sequence of channel indicator measurements. The channel detection module decides whether to perform a channel detection by a trigger that reflects the prediction effect. The channel selection module performs channel selection based on the channel coefficients of the prediction results. We use a commercial reader to collect data in a real environment, and build an EDChannel model based on the deep learning module of Tensorflow and Keras. As a result, we have implemented the channel prediction module and completed the overall channel selection process. The experimental results show that the EDChannel algorithm has higher prediction accuracy than the previous state-of-the-art methods. The overall throughput of our scheme is improved by approximately 2.9% and 14.1% over Zhao’s scheme in both stable and unstable environments.

Energy Efficiency Maximization in the Wireless-Powered Backscatter Communication Networks with DF Relaying

This paper focuses on the design of an optimal resource allocation scheme to maximize the energy efficiency (EE) in the wireless-powered backscatter communication networks (WPBCN) with decode and forward (DF) relaying. The two different devices are supported to operate in different modes, the harvest-then-transmit (HTT) mode and backscatter communication (BackCom) mode, respectively. In particular, we formulate an optimization problem to maximize system EE by jointly optimizing the transmit power of hybrid access point (H-AP) and the system time resource allocation. To deal with the nonconvex problem, we investigate the characteristics of the EE expression and a variable substitution approach. Then, the optimal power allocation scheme and iterative optimization algorithm were derived for achieving maximum EE. Extensive simulation results have demonstrated that the system EE can be improved about 10% because the proposed scheme provides more flexibility to utilize the resource efficiently by employing the proposed scheme.

Physical Layer Security of Two-Way Ambient Backscatter Communication Systems

Achieving high network traffic demand in limited spectrum resources is a technical challenge for the beyond 5G and 6G communication systems. To this end, ambient backscatter communication (AmBC) is proposed for Internet-of-Things (IoT), because the backscatter device (BD) can realize communication without occupying extra spectrum resources. Moreover, the spectral efficiency can be further improved by using two-way (TW) communication. However, secure communication is a great challenge for accessing massive IoT devices due to the broadcasting nature of wireless propagation environments. In light of this fact, this article proposed a two-way ambient backscatter communication (TW-AmBC) network with an eavesdropper. Specifically, the physical layer security (PLS) is studied through deriving the analytical/asymptotic expressions of the outage probability (OP) and intercept probability (IP). Moreover, the outage probabilities (OPs) in high signal-to-noise ratio (SNR) regions are studied for the asymptotic behavior and the intercept probabilities (IPs) in high main-to-eavesdropping ratio (MER) regions as well. Through analysis and evaluation of simulation performance, the results show that (i) when considering the target node, the OP of the BD decreases with increasing SNR, that is, enhancing the reliability; (ii) an optimal value of BD’s reflection coefficient that maximize the reliability of backscatter link can be obtained; (iii) in high SNR regions, the OPs approach a constant; thus, the diversity orders are zero; (iv) when increasing the MER, the IP of target node decreases, suggesting the security enhances; (v) a trade-off exists between reliability and security which can be optimized by carefully designing the parameters.