Backscatter Networks Research Articles

Access Control for Ambient Backscatter Enhanced Wireless Internet of Things

Beyond fifth-generation (B5G) and future networks face the challenges of spectral, energy and cost efficiency for large-scale machine-type communications. Recently, emerging ambient backscatter communication (AmBC) technology provides a promising paradigm for the development of green Internet of Things (IoT) networks in B5G era. Unlike existing work on AmBC which mostly focuses on physical layer with relatively ideal model, i.e., classic three-nodes model composed of radio frequency (RF), backscatter device (BD) and IoT device, this paper studies the access control strategy, including coefficient design and device association, from the perspective of networking. Assuming whether channel information is available a-priori, we propose online and offline access control strategies respectively. For offline access control strategy, we leverage the difference of two convex functions approximation (DCA) and dual decomposition to transform the non-concave optimization problem into the concave one, and design a distributed access control strategy called DCA-S. Furthermore, for the case that channel information is assumed to be unknown in advance due to the dynamics of primary and backscatter networks, we design a combinatorial multi-armed bandit (CMAB) access control strategy (CMAB-S). Numerical results show that the proposed DCA-S and CMAB-S can achieve significant performance improvement of the system in both cases of available and unavailable channel information compared with benchmark schemes.

RAEN: Rate Adaptation for Effective Nodes in Backscatter Networks.

A backscatter network, as a key enabling technology for interconnecting plentiful IoT sensing devices, can be applicable to a variety of interesting applications, e.g., wireless sensing and motion tracking. In these scenarios, the vital information-carrying effective nodes always suffer from an extremely low individual reading rate, which results from both unpredictable channel conditions and intense competition from other nodes. In this paper, we propose a rate-adaptation algorithm for effective nodes (RAEN), to improve the throughput of effective nodes, by allowing them to transmit exclusively and work in an appropriate data rate. RAEN works in two stages: (1) RAEN exclusively extracts effective nodes with an identification module and selection module; (2) then, RAEN leverages a trigger mechanism, combined with a random forest-based classifier, to predict the overall optimal rate. As RAEN is fully compatible with the EPC C1G2 standard, we implement the experiment through a commercial reader and multiple RFID tags. Comprehensive experiments show that RAEN improves the throughput of effective nodes by 3×, when 1/6 of the nodes are effective, compared with normal reading. What is more, the throughput of RAEN is better than traditional rate-adaptation methods.

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.

Interference-Free Source Deployment for Coverage in Underwater Acoustic Backscatter Networks

Interference-Free Source Deployment for Coverage in Underwater Acoustic Backscatter Networks

Throughput fairness in cognitive backscatter networks with residual hardware impairments and a nonlinear EH model

This paper is to design a throughput fairness-aware resource allocation scheme for a cognitive backscatter network (CBN), where multiple backscatter devices (BDs) take turns to modulate information on the primary signals and backscatter the modulated signals to a cooperative receiver, while harvesting energy to sustain their operations. The nonlinear energy harvesting circuits at the BDs and the residual hardware impairments at the transceivers are considered to better reflect the properties of the practical energy harvesters and transceivers, respectively. To ensure the throughput fairness among BDs, we formulate an optimization problem to maximize the minimum throughput of BDs by jointly optimizing the transmit power of the primary transmitter, the backscattering time and reflection coefficient for each BD, subject to the primary user’s quality of service and BDs’ energy-causality constraints. We introduce the variable slack and decoupling methods to transform the formulated non-convex problem, and propose an iterative algorithm based on the block coordinate descent technique to solve the transformed problem. We also investigate a special CBN with a single BD and derive the optimal solution in the closed form to maximize the BD’s throughput. Numerical results validate the quick convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.

Outage Analysis for Tag Selection in Reciprocal Backscatter Communication Systems

Applying tag selection to backscatter networks can greatly improve the system outage performance. In this letter, the analytical expressions on outage probability for both ideal and outdated tag selection in reciprocal backscatter networks are derived with a truncated series expansion. Furthermore, the asymptotic analysis with large transmission power is conducted to give an insight into the effects of system parameters. From the asymptotic analysis, it is found that the diversity order of outage probability for ideal tag selection is equal to half of the number of tags. However, when applying outdated tag selection, the diversity order is equal to one, and thereby no diversity gain can be harvested, regardless of the temporal correlation and the number of tags.

Interference-Aware Mobile Backscatter Communication: A PHY-Assisted Rate Adaptive Approach

Over the past decade, backscatter nodes have received booming interest for many emerging mobile applications, such as sports analytics and interactive gaming. However, backscatter networks are not ready to provide a high-throughput and stable communication platform for billions of such mobile nodes due to two main factors in rate adaptation. First, the common mapping paradigm that chooses the optimal rate based on RSSIs is hardly adaptable to hardware diversity. Second, the current probing processes are not optimized for mobile scenarios due to inefficient probing trigger, inaccurate channel estimation, and unique self-interference. To address those issues, we propose MobiRate, a mobility-aware rate adaptation link-layer that fully exploits the mobility hints from PHY information to deliver a high-throughput link-layer for mobile backscatter networks. The key insight is that mobility-hints can greatly benefit link-layer design, including rate selection and channel probing. Specifically, we introduce a novel velocity-based loss-rate estimation module, a mobility-assisted probing trigger, a selective probing module, and a robust self-interference detection module, significantly saving probing time and improving probing accuracy. As MobiRate is fully compatible with the current standard, we prototype it using COTS RFID readers and commercial tags. Our extensive experiments demonstrate that MobiRate can successfully identify self-interference with detection accuracy over 90% for tags of different velocities. Moreover, it achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges

The exponential growth of Internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions. Complicated and dynamic SS systems can be exposed to different potential security and privacy issues, requiring protection mechanisms to be adaptive, reliable, and scalable. Machine learning (ML) based methods have frequently been proposed to address those issues. In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms. In particular, we elaborate the state-of-the-art methodologies for improving the performance of SS communication systems for various vital aspects, including ML based cognitive radio networks (CRNs), ML based database assisted SS networks, ML based LTE-U networks, ML based ambient backscatter networks, and other ML based SS solutions. We also present security issues from the physical layer and corresponding defending strategies based on ML algorithms, including Primary User Emulation (PUE) attacks, Spectrum Sensing Data Falsification (SSDF) attacks, jamming attacks, eavesdropping attacks, and privacy issues. Finally, extensive discussions on open challenges for ML based SS are also given. This comprehensive review is intended to provide the foundation for and facilitate future studies on exploring the potential of emerging ML for coping with increasingly complex SS and their security problems.

Spatial Modulation Based Multiple Access for Ambient Backscatter Networks

Both spatial modulation (SM) and spatial multiple access (SMA) exploit spatial differences between antennas to implement modulation and multiple access. In this sense, this letter designs an SM-SMA system for ambient backscatter communication networks. SM activates a part of a tag’s antennas to carry information, and thus decreases the interference between antennas in SMA. We then propose a modified-maximum likelihood (M-ML) detector to separate and obtain and the backscattered signal without the need of recovering the source signal. By exploiting the sparse characteristics of the SM-SMA signal, a multi-user sparse Bayesian learning (MSBL) based detector is further proposed to reduce the complexity of M-ML. The simulation results verify that SM-SMA achieves a lower bit error rate than SMA, and the MSBL based detector has a lower complexity than M-ML at the cost of increasing BER.

Distributed Resource Allocation in RF-Powered Cognitive Ambient Backscatter Networks

Ambient backscatter communications has shown the great potentials to achieve spectrum and energy efficient communications for future wireless networks (e.g., 6G). Besides, to fully exploit the ambient RF environment, backscatter communications can be combined with RF-powered cognitive networks which brings more reliable and flexible communications. In such networks, a cognitive user pair could communicate through active transmission or backscattering existing signals. In this work, we investigate the distributed resource allocation in terms of channel selection and backscatter power allocation in such setting. A unified framework integrating stochastic geometry and evolutionary game is proposed to model and analyze the distributed resource allocation. Specifically, stochastic geometry is used to analyze the average performance for users considering different communication modes. Then with the average performance obtained, evolutionary game theory is used to model the competitive resource allocation, and replicator dynamics is used to capture the dynamic change of user selections. Based on the formulated evolutionary game, a distributed channel selection and backscatter power allocation algorithm is proposed. Simulations have been performed which validate the theoretical analysis. Also the effectiveness of the proposed scheme is verified.

Power Beacon Placement for Maximizing Guaranteed Coverage in Bistatic Backscatter Networks

The bistatic backscatter architecture, with its extended range, enables flexible deployment opportunities for backscatter devices. In this paper, we study the placement of power beacons (PBs) in bistatic backscatter networks to maximize the guaranteed coverage distance (GCD), defined as the distance from the reader within which backscatter devices are able to satisfy a given quality-of-service constraint. This work departs from conventional energy source placement problems by considering the performance of the additional backscatter link on top of the energy transfer link. We adopt and optimize a symmetric PB placement scheme to maximize the GCD. The optimal PB placement under this scheme is obtained using either analytically tractable expressions or an efficient algorithm. Numerical results provide useful insights into the impacts of various system parameters on the PB placement and the resulting GCD, plus the advantages of the adopted symmetric placement scheme over other benchmark schemes.

5.8-GHz Low-Power Tunnel-Diode-Based Two-Way Repeater for Non-Line-of-Sight Interrogation of RFIDs and Wireless Sensor Networks

The efficacy of the deployment of backscatter networks in sensor networks and the internet of things has been severely limited by the maximum read range of backscatter radio frequency identification (RFID) tags. The work presented here demonstrates a low-power two-way repeater architecture capable of increasing the read range and extending the coverage of tags to non-line-of-sight (NLOS) scenarios. This is achieved through the use of a two-way frequency divided tunnel diode-based reflection amplifier optimized for use in backscattering channels. The system reports a more than 2X increase in the read range of a semi-passive RFID tag at 17 dBm output power. The work presented here shows a system capable of giving a comparable performance with the state of the art while consuming up to 1000X less power in a simple single-component front end.

On Energy Allocation and Data Scheduling in Backscatter Networks With Multi-Antenna Readers

In this paper, we study the throughput utility functions in buffer-equipped monostatic backscatter communication networks with multi-antenna Readers. In the considered model, the backscatter nodes (BNs) store the data in their buffers before transmission to the Reader. We investigate three utility functions, namely, the sum, the proportional and the common throughput. We design online admission policies, corresponding to each utility function, to determine how much data can be admitted in the buffers. Moreover, we propose an online data link control policy for jointly controlling the transmit and receive beamforming vectors as well as the reflection coefficients of the BNs. The proposed policies for data admission and data link control jointly optimize the throughput utility, while stabilizing the buffers. We adopt the min-drift-plus-penalty (MDPP) method in designing the control policies. Following the MDPP method, we cast the optimal data link control and the data admission policies as solutions of two independent optimization problems which should be solved in each time slot. The optimization problem corresponding to the data link control is non-convex and does not have a trivial solution. Using Lagrangian dual and quadratic transforms, we find a closed-form iterative solution. Finally, we use the results on the achievable rates of finite blocklength codes to study the system performance in the cases with short packets. As demonstrated, the proposed policies achieve optimal utility and stabilize the data buffers in the BNs.

Intelligent Spectrum Management and Trajectory Design for UAV‐Assisted Cognitive Ambient Backscatter Networks

In this paper, we consider a novel Internet of Things (IoT) system in smart city called unmanned aerial vehicle‐ (UAV‐) assisted cognitive backscatter network, where a UAV is employed as both a relay and a radio frequency source to help the data transmission between ground IoT backscatter devices (BDs) and a remote data center (DC). However, since the IoT applications are usually not assigned dedicated spectrum resource in smart cities, these data transmissions from BDs to the DC should share the licensed spectrum of cellular users (CUs). Therefore, we aim to maximize the minimum uplink throughput among all BDs while avoiding severe interference to CUs via joint spectrum management and UAV trajectory design. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into two blocks. For the spectrum management problem, we first prove its convexity with the transmit power and time scheduling and then propose a two‐step method to solve the two variables sequentially. For the UAV trajectory design problem, we resort to the fractional programming method to handle it. Simulation results demonstrate that the proposed algorithm can significantly increase the average max‐min rate of the BDs while guaranteeing the acceptable interference to CUs with a fast convergence speed.

QuadScatter: Computational Efficiency in Simultaneous Transmissions for Large-Scale IoT Backscatter Networks

Backscatter communication has attracted attention owing to its ultra-low-power consumption ability, which is expected to enhance internet of things (IoT) technology that aims to enable many novel applications for object-to-object communication. Such a network with a large and continuously increasing number of connected objects will benefit significantly from resource-saving. This work introduces a system named QuadScatter, which is a set of algorithms that select and associate transmitters, tags and readers to enable simultaneous backscatter transmissions and increase network capacity. Consequently, the energy consumption in the network is considerably lessened. Intensive simulations have been conducted to demonstrate the effectiveness of backscatter simultaneous transmissions. QuadScatter shows promising results compared to the exhaustive search algorithm. The simulation results highlight computational time and simultaneous transmission improvements of at least $ 250\rm \times$ and $ 2\rm \times$ , respectively. Furthermore, while the exhaustive search is limited to a few nodes ( $ ), our proposal uses numerous nodes. Additionally, an implementation of limited simultaneous backscatter transmissions is conducted to show its feasibility in the real world.

High-Throughput and Robust Rate Adaptation for Backscatter Networks

Recently backscatter networks have received booming interest because, they offer a battery-free communication paradigm using propagation radio waves as opposed to active radios in traditional sensor networks while providing comparable sensing functionalities, ranging from light and temperature sensors to recent microphones and cameras. While sensing data on backscatter nodes has been seen on a clear path to increasing in both volume and variety, backscatter communication is not well prepared and optimized for transferring such continuous and high-volume data. To bridge this gap, we propose a high-throughput rate adaptation scheme for backscatter networks by exploring the unique characteristics of backscatter links and the design space of the ISO 18000-6C (C1G2) protocol. Our key insight is that while prior work has left the downlink unattended, we observe that the quality of downlink is affected significantly by multipath fading and thus can degrade the uplink and overall throughput considerably. Therefore, we introduce a novel rate mapping algorithm that chooses the best rate for both the downlink and uplink. Also, we design an efficient channel estimation method fully compatible with the C1G2 protocol and a reliable probing trigger, substantially saving probing overhead. To combat interference, we further design an interference detector using clusters and lightweight countermeasures to make rate adaptation more robust. Our scheme is prototyped using commercial RFID readers and tags. The results show that we can achieve up to $2.6\times $ throughput gain over state-of-the-art approaches across various mobility, channel, network-size, and interference conditions.

Energy Efficiency for RF-Powered Backscatter Networks Using HTT Protocol

Backscatter radio is an intriguing technology for Internet of Things applications, which enables ultra-low-power devices to communicate with each other. Most of the existing works on the combination of the backscatter communications and the “harvest-then-transfer” (HTT) protocol focus on the throughput maximization, yet the study on the energy efficiency (EE) maximization is still lacking. Driven by this observation, this paper studies the EE maximization for an RF-powered backscatter network with the HTT protocol. Multiple hybrid devices (HDs) can either use the backscatter mode by backscattering energy signals from the power beacon (PB) to the reader or use the HTT protocol to first harvest energy from the PB, and then to transmit data to the reader. The proposed scheme aims to optimize multiple parameters including PB's transmit power and time, HDs' transmit power and time allocation between the backscatter mode and the HTT mode for maximizing the EE of all the HDs. An iterative algorithm is developed to find the optimal solution for the proposed resource allocation scheme. Simulation results demonstrate the superiority of the proposed scheme in terms of EE of all the HDs.

Max-Min Energy-Efficient Resource Allocation for Wireless Powered Backscatter Networks

In this letter, we present the first attempt to solve an energy efficiency (EE) based max-min fairness problem for a wireless powered backscatter network where a power beacon (PB), which is a dedicated radio frequency (RF) power resource, and multiple backscatter devices work in the same frequency band. Each backscatter transmitter harvests energy from the signal transmitted by the PB, modulates its own information on the received signal, and backscatters the modulated signal to its associated receiver. We propose to ensure max-min fairness among the backscatter links by jointly optimizing the PB transmission power and the backscatter reflection coefficients. For analytical tractability, we solve the optimization problem for the case of two co-channel backscatter links by employing Lagrange dual decomposition when it is convex, and analyzing the monotonicity of the constraints when it is non-convex. Based on the obtained closed-form expressions of the optimal PB transmission power and the optimal backscatter reflection coefficients, we propose an iterative algorithm for max-min EE resource allocation. Simulation results show that the proposed iterative algorithm converges very fast and achieves a much fairer EE performance among backscatter links than maximizing the system EE of the network.

Channel Prediction Based on BP Neural Network for Backscatter Communication Networks.

Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.

Resource Allocation for Full-Duplex-Enabled Cognitive Backscatter Networks

Ambient backscatter communications (AmBC) enable wireless communications riding on ambient radio frequency (RF) signals instead of self-generated RF signals. Therefore, it has been considered as a promising candidate for the future Internet-of-Things with stringent energy and spectrum constraints. In this paper, we investigate a full-duplex-enabled cognitive backscatter network, in which an AmBC system underlays a primary cellular system, and the primary access point can transmit primary signals and receive backscatter signals simultaneously via full-duplex communications. We aim to maximize the throughput of the AmBC system while guaranteeing the minimum rate requirements of the primary system via joint time scheduling, transmit power allocation, and reflection coefficient (RC) adjustment. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into the time scheduling variable and the joint transmit power allocation and RC adjustment variable. For the time scheduling problem, we first prove its convexity and then utilize the interior-point method to solve it. For the joint power allocation and RC adjustment problem, we resort to the concave-convex procedure to transform it into a sequence of convex optimization problems, and then adopt Lagrange dual decomposition to tackle these convex optimization problems. The simulation results demonstrate that the proposed method can significantly increase the throughput of the AmBC system with a fast convergence speed.