Wireless-Powered Backscatter Communication 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

Robust Energy Efficiency Optimization for Double-RIS-Assisted Wireless-Powered Backscatter Communications

Robust Energy Efficiency Optimization for Double-RIS-Assisted Wireless-Powered Backscatter Communications

Robust Resource Allocation for Wireless-Powered Backscatter Communication Systems With NOMA

Robust Resource Allocation for Wireless-Powered Backscatter Communication Systems With NOMA

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.

Wireless-powered backscatter communications for smart sustainable cities: A secrecy analysis

Due to its ability to enable self-sustainable communications, wirelessly powered backscatter communication (WP-BackCom) has been deemed a promising technology for supporting smart sustainable cities. We note that the existing works on analyzing the secrecy performance of WP-BackComs have largely ignored the spatial randomness of nodes that is used to model a large-scale network. To fill this gap, this paper evaluates the secrecy outage probability of the large-scale WP-BackComs based on a tractable framework of stochastic geometry (SG). In particular, the imperfect successive interference cancellation (SIC) at each gateway (GW) and eavesdropper (Eve), and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU) are considered. The energy outage probability and secrecy outage probability expressions are derived in the general case at first, then the closed-form expressions are obtained under the special scenario, i.e., the no-noise and the typical propagation loss scenario. Numerical simulations validate the analytical results and examine the impacts of various parameters on the secrecy performance. In particular, there exists an optimal reflection coefficient for secrecy outage probability minimization under certain conditions, and the optimal reflection coefficient increases with the increasing SIC coefficient at GW.

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.

Stochastic Geometry Based Performance Study for Wireless-Powered Backscatter Communications

Wirelessly powered backscatter communication (WP-BackCom) enables battery-free Internet of Things (IoT) nodes to passively modulate and backscatter information on the energy signals transmitted by power beacons (PBs) and to harvest energy for realizing energy self-sustainability. It has been deemed a promising solution for the large-scale deployment of IoT nodes. In this paper, we exploit stochastic geometry (SG) to develop a tractable framework to evaluate the successful transmission probability in WP-BackCom while considering the imperfect successive interference cancellation (SIC) at each gateway (GW) and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU). The successful transmission probability is defined as the probability for the event that not only the BU can harvest sufficient power from PBs for sustaining its operation but also the backscattered information can be successfully decoded by the GW. We derive the active probability into a closed form and then obtain the successful transmission probability based on the random spectrum access (RSA) policy. The closed-form expression for the successful transmission probability is derived under three special cases. We also devise a closed-form expression for the sub-optimal reflection coefficient to obtain a sub-optimal successful transmission probability. The analysis reveals that the sub-optimal reflection coefficient increases with the SIC coefficient. Numerical simulations validate the correctness of the above theoretical derivations and examine the impacts of various parameters on the successful transmission probability.

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.

UGV-Assisted Wireless Powered Backscatter Communications for Large-Scale IoT Networks

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency.

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.

Chance-Constrained Resource Allocation for Cognitive Wireless-Powered Backscatter Communication Networks

Chance-Constrained Resource Allocation for Cognitive Wireless-Powered Backscatter Communication Networks

Robust Energy-Efficient Optimization for Secure Wireless-Powered Backscatter Communications With a Non-Linear EH Model

Secure and fair transmission is essential for wireless-powered backscatter communication networks (WPBCNs), which has not been taken seriously enough by the existing works. In this letter, we consider a multi-tag WPBCN in the presence of an eavesdropper, where a power station (PS) serves multiple tags which backscatter wireless information to a reader. Furthermore, the artificial noise (AN) is transmitted from the PS to impair the eavesdropping capability for security deliberately. An energy efficiency (EE)-based resource allocation problem with max-min fairness is formulated by jointly optimizing the backscattering time, the power ratio of the AN, and the reflection coefficient of each tag, where imperfect channel state information and non-linear energy harvesting models are considered. To tackle its non-convexity, we propose a Dinkelbach-based iterative algorithm to obtain the sub-optimal solution. Simulation results verify the effectiveness of the proposed algorithm.

Joint Computation Offloading and Radio Resource Allocation in MEC-Based Wireless-Powered Backscatter Communication Networks

The multi-access edge computing (MEC)-based wireless-powered backscatter communication networks (WP-BackComNets) allow wireless devices (WDs) to offload computation resources to lightweight and widely deployed MEC servers with the assistance of backscatter devices (BDs), which have substantial application prospects for the emerging Internet-of-Things applications. However, the limited battery capacity of WDs is one of the bottlenecks restricting its further development. Reducing the energy consumption and the computation burden of WDs while ensuring the quality-of-service requirements is an urgent issue. To this end, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs for an MEC-based WP-BackComNet by jointly optimizing user association, the transmit power and transmission time of WDs, the computational offloading coefficient of each task, and the reflection coefficients of BDs, where the circuit power consumption of BDs, the computational capabilities of WDs, and the task execution delay budgets are considered. To handle this non-convex problem, we propose an efficient algorithm to obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can effectively decrease the energy consumption compared with the benchmarks.

Performance Analysis of Wireless-powered Backscatter Communication with TSR-based Relay

Performance Analysis of Wireless-powered Backscatter Communication with TSR-based Relay

Rate-Energy Tradeoffs of Wireless Powered Backscatter Communication With Power Splitting and Time Switching

We consider a monostatic wireless powered backscatter communication (WPBC) system, where a device performs backscatter modulation and energy harvesting (EH) using the received signal from a multi-antenna hybrid access point. The rate-energy regions of the WPBC are studied for power splitting (PS) and time switching (TS) receivers in a comprehensive way by using the rate and EH functions reflecting the effects of practical systems. For both linear and nonlinear EH models, we analyze the rate-energy regions of the static PS and TS strategies and optimize the dynamic strategies exploiting the channel state information (CSI) to extend the regions. The regions with the static strategies are obtained in closed-form expressions and the dynamic strategies are obtained in explicit forms as a function of a dual variable, by approximating the nonlinear EH model to a piecewise linear EH (PLEH) model. The results show that the dynamic PS and TS strategies outperform their static counterparts and their gains are more prominent in the sensitivity region of a nonlinear EH circuit and with a smaller number of antennas. It is also observed that the dynamic PS strategy provides the best performance but can be replaced by the static one for a large number of antennas to avoid the CSI. The strategies obtained explicitly by using the PLEH model are also shown to work well with a practical EH model without a noticeable loss in the performance.

Throughput Fairness Guarantee in Wireless Powered Backscatter Communications With HTT

Harvest-then-transmit (HTT) and backscatter communications have different tradeoffs between energy harvesting and data transmission and may complement each other for increasing the achievable throughput, which however has not been sufficiently exploited by the existing works. In this letter, we propose a max-min throughput based resource allocation scheme for a wireless powered Internet of Things (IoT) network to ensure the fairness among multiple sensor nodes (SNs), which harvest energy from the signals transmitted by a power beacon (PB) and convey their information to an information receiver (IR) by backscattering, HTT, or the hybrid of them. We introduce a series of auxiliary variables to transform the non-convex max-min throughput problem into a convex one. We show that the max-min throughput is achieved when the SN with the worst link, which achieves the minimum throughput, consumes all the harvested energy and works in the hybrid of backscattering and HTT. Simulation results show that compared with the sum-throughput maximization scheme, our developed resource allocation scheme achieves a much better throughput fairness, while maintaining almost the same average throughput.

User Cooperation in Wireless-Powered Backscatter Communication Networks

In this letter, we introduce new user-cooperation schemes for wireless devices in a wireless-powered backscatter communication network with the aim to improve communication and energy efficiency for the whole network. In particular, we consider two types of wireless devices which can support different communication modes, i.e., backscatter and harvest-then-transmit, and they can cooperate to deliver the information to the access point. To improve energy transmission efficiency for the devices, energy beamforming is deployed at the power beacon. We then formulate the weighted sum-rate maximization problem by jointly optimizing time schedule, power allocation, and energy beamforming. Due to the non-convex issue of the optimization problem, we employ the variable substitutions and semidefinite relaxation techniques to obtain the optimal solution. Simulation results show that the proposed cooperation framework can significantly improve the communication efficiency compared with non-cooperation approach.

Energy-Efficient Resource Allocation for Wirelessly Powered Backscatter Communications

In this letter, we consider a wireless-powered backscatter communication (WP-BackCom) network, where the transmitter first harvests energy from a dedicated energy RF source in the sleep state, and then backscatters information and harvests energy simultaneously through a reflection coefficient. Our goal is to maximize the achievable energy efficiency of the WP-BackCom network via jointly optimizing time allocation, reflection coefficient and transmit power of the dedicated energy RF source. The optimization problem is non-convex and challenging to solve. We develop an efficient Dinkelbach-based iterative algorithm to obtain the optimal resource allocation scheme. The study shows that for each iteration, the energy-efficient WP-BackCom network is equivalent to either the network in which the transmitter always operates in the active state, or the network in which the dedicated energy RF source adopts the maximum allowed power.

Optimal Time Scheduling for Wireless-Powered Backscatter Communication Networks

This letter introduces a novel wireless-powered backscatter communication system which allows sensors to utilize RF signals transmitted from a dedicated RF energy source to transmit data. In the proposed system, when the RF energy source transmits RF signals, the sensors are able to backscatter the RF signals to transmit data to the gateway and/or harvest energy from the RF signals for their operations. By integrating backscattering and energy harvesting techniques, we can optimize the network throughput of the system. In particular, we first formulate the time scheduling problem for the system, and then propose an optimal solution using convex optimization to maximize the overall network throughput. Numerical results show a significant throughput gain achieved by our proposed design over two other baseline schemes.

Backscatter Communications for Wireless Powered Sensor Networks With Collision Resolution

Wireless powered backscatter communications is an attractive technology for next-generation low-powered sensor networks such as the Internet of Things. However, backscattering suffers from collisions due to multiple simultaneous transmissions and a dyadic backscatter channel, which greatly attenuate the received signal at the reader. This letter deals with backscatter communications in sensor networks from a large-scale point-of-view and considers various collision resolution techniques: directional antennas, ultra-narrow band transmissions, and successive interference cancellation. We derive analytical expressions for the decoding probability and our results show the significant gains, which can be achieved from the aforementioned techniques.