Ambient Radio Frequency Signals Research Articles

An Overview on Backscatter Communications

As a key low-power communication technique, backscatter communications exploits the reflected or backscattered signals to transmit data, where the backscattered signals can be the reflection of ambient radio frequency (RF) signals, the RF signals from the dedicated carrier emitter or the signal photons in the non-classical quantum entangled pairs, etc. In the past 70 years, various kinds of backscatter communication systems have been developed, which will enable the low-power communications as required in the Internet of things (IoTs) and green communications. This article provides a historical view on the development and the research achievements on backscatter communications, including the fundamental principles, the applications, the challenges, and the potential research topics. This article will benefit the researchers and engineers concerning the area of backscatter communications, especially for applications in IoTs.

Noncoherent Backscatter Communications Over Ambient OFDM Signals

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for the Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient radio-frequency signals generated by legacy communication systems such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy orthogonal frequency division multiplexing (OFDM) signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higher-order modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the $M$ -ary case. We corroborate our analysis using Monte–Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in this paper for backscatter communication over ambient OFDM signals in different scenarios.

Optimal Operating Mode Selection of Ambient Backscatter Communication Systems

Ambient backscatter (AB) communication has emerged as an energy-preserving technology in Internet of Things (IoT). Specifically, an AB transmitter communicates with its receiver by backscattering ambient radio frequency (RF) signals instead of active information transmissions. When not operating in the communication mode, the AB transmitter can instead harvest the RF energy to replenish its battery, e.g., for supplying sensing energy consumptions. In this paper, we consider the optimal operating mode selection problem of the AB system that the transmitter decides either backscatter information or harvest energy based on the time-varying ambient RF signal strength. In particular, we aim to maximize the long-term average communication data rate given that an average energy-harvesting budget constraint is met. We formulate the optimization as a 0-1 knapsack problem and propose a dynamic programming (DP) based solution algorithm and a reduced-complexity greedy algorithm. Furthermore, we simulate under various setups, and show that the two proposed algorithms can achieve significant performance improvement over other representative benchmark methods.

Optimal Time Scheduling Scheme for Wireless Powered Ambient Backscatter Communications in IoT Networks

In this paper, we investigate optimal schemes to manage time scheduling of multiple modules, including spectrum sensing, radio frequency (RF) energy harvesting (RFH) and ambient backscatter communication (ABCom) by maximizing data transmission rate in Internet of Things networks. We first detect ambient RF signals with high signal power as the RF resource of RFH and ABCom by using spectrum sensing with energy detection techniques. Specifically, compressive sensing (CS) is adopted to detect the wideband RF signals with improving spectrum sensing efficiency at the same time. We formulate a joint optimization problem to manage time scheduling parameter and power allocation ratio. In addition, we propose to find the threshold of spectrum sensing for ABCom communications by analyzing the outage probability of backscatter communications. Numerical results demonstrate that the optimal schemes using spectrum sensing are achieved with better transmission rates. The designed time scheduling scheme with CS is confirmed to be more efficient, and the superiorities become more obvious with the increase of network operation time. Moreover, the optimal scheduling parameters and power allocation ratios are obtained. Simulations illustrate that the threshold of spectrum sensing for backscatter communications is obtained by analyzing the outage probability of backscatter communications.

An Efficient Broadband Slotted Rectenna for Wireless Power Transfer at LTE Band

A compact and broadband rectifying antenna (rectenna) which consists of a novel slotted antenna and an efficient rectifying circuit is presented for wireless power transfer at LTE-2300/2500 band. Three different broadband antennas have been investigated and compared. It is found that the newly designed antenna which combines the annular slot radiation patch and a slotted ground plane (GP) has the widest bandwidth and the best matching characteristic. A new broadband dual-stub matching network is proposed to maximize the transmission of wireless power and enhance the reliability of the rectifier when the input power and load varies. In addition, the ground of the rectifier is directly connected to the GP of the antenna for compactness and low energy loss. The proposed designs have been simulated, fabricated, and tested. The results indicate that the proposed receiving antenna performs well at LTE band with a wide bandwidth from 2 to 3.1 GHz. The maximum efficiency of the proposed rectenna reaches 70% at 2.5 GHz under the input power of 5 dBm. Furthermore, the rectenna remains at a relatively efficient power conversion when the load varies in a wide range from 2 to 20 kΩ under different low input power, which is of great significance when the ambient radio frequency(F) signal is weak. Therefore, the rectenna is potentially applicable to a wide range of low-power electronic devices by harvesting RF energy at LTE band.

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

Internet of Things (IoT) devices can apply mobile-edge computing (MEC) and energy harvesting (EH) to provide the satisfactory quality of experiences for computation intensive applications and prolong the battery lifetime. In this article, we investigate the computation offloading for IoT devices with energy harvesting in wireless networks with multiple MEC devices such as base stations and access points, each with different computation resource and radio communication capability. We propose a reinforcement learning based computation offloading framework for an IoT device to choose the MEC device and determine the offloading rate according to the current battery level, the previous radio bandwidth to each MEC device and the predicted amount of the harvested energy. A "hotbooting" Q-learning based computation offloading scheme is proposed for an IoT device to achieve the optimal offloading performance without being aware of the MEC model, the energy consumption and computation latency model. We also propose a fast deep Q-network (DQN) based offloading scheme, which combines the deep learning and hotbooting techniques to accelerate the learning speed of Q-learning. We show that the proposed schemes can achieve the optimal offloading policy after sufficiently long learning time and provide their performance bounds under two typical MEC scenarios. Simulations are performed for IoT devices that use wireless power transfer to capture the ambient radio-frequency signals to charge the IoT batteries. Simulation results show that the fast DQN-based offloading scheme reduces the energy consumption, decreases the computation delay and the task drop ratio, and increases the utility of the IoT device in dynamic MEC, compared with the benchmark Q-learning based offloading.

Design and Analysis of a Distributed and Demand-Based Backscatter MAC Protocol for Internet of Things Networks

Backscatter communication is a new wireless communication technology, which can enable battery-free devices to communicate with others by backscattering ambient radio-frequency signals, and therefore has great potential to be deployed in the future Internet of Things (IoT) networks. In the existing related studies, the adopted centralized approach might be not suitable for a large-scale IoT network with sporadic backscatter communication, while the adopted distributed approach ignored the demand of the access point (AP). In this paper, we consider a large-scale IoT network consisting of the legacy Wi-Fi communication and the backscatter communication, and aim to propose an efficient distributed backscatter medium access control protocol that takes into account of the demand of the AP. In our protocol, when the AP has a demand to collect the information of backscatter devices, it enables backscatter devices to contend for the channel with Wi-Fi devices in a separate manner, where the backscatter devices are only allowed to participate in the contention in a limited time period. We then characterize this constrained contention process and develop a theoretical model to analyze the performance of the proposed protocol. With this model, we can exactly express the per-node throughput of Wi-Fi and backscatter devices. Finally, extensive simulations verify that our model is much accurate, and our protocol can outperform the related protocol in terms of both per-node throughput and system throughput.

Joint Data-Energy Beamforming and Traffic Offloading in Cloud Radio Access Networks With Energy Harvesting-Aided D2D Communications

In this paper, a simultaneous wireless information and power transfer-based traffic offloading scheme is proposed for cloud radio access networks with energy harvesting-aided device-to-device (D2D) communications. Wherein, the traffic offloading via D2D communications is utilized to alleviate the heavy burden on the capacity-limited fronthauls, while the energy harvesting design is adopted to stimulate offloading by compensating the energy consumption at the D2D transmitters. Different from the conventional works that harvest energy from ambient radio-frequency signals, dedicated energy signals are designed to provide a more flexible wireless power supply for the D2D transmitters with the data-energy beamforming technique. However, transmitting the dedicated energy signals impairs the performances of the wireless information transfer. To achieve a better balance between the traffic offloading gains and the wireless power transfer costs, a weighted sum-rate maximization problem is formulated. Nevertheless, the binary variables introduced by the offloading decision and the constraints on the data-energy beamforming make the optimization problem non-convex. To solve this intractable problem, a layered optimization method with the iterative optimizing and the ellipsoid method is proposed. Furthermore, an algorithm with lower complexity is proposed with a separated data-energy beamforming design. Simulation results reveal that a significant sum-rate gain can be achieved via the proposed scheme.

Energy efficiency maximisation in wireless powered networks with cooperative non‐orthogonal multiple access

In the 5G wireless networks, non-orthogonal multiple access (NOMA) is a promising paradigm to improve its high spectrum efficiency. This study considers applying simultaneous wireless information and power transfer (SWIPT) technique to cooperative NOMA wireless networks, where energy-constrained relay nodes harvest the ambient radio-frequency signal and use the harvested energy to forward the packets from sources to destinations. To this end, the authors first formulate the energy-efficient cooperative transmission problem for SWIPT in NOMA with imperfect channel state information. Then, by exploiting alternative optimisation and dual decomposition, they design an iterative algorithm for power allocation and power splitting (PS) to solve the non-convex optimisation problem. The authors' simulation results reveal that the proposed algorithm can converge within a few iterations and yield optimal system energy efficiency.

Proportional Fair Energy-Efficient Resource Allocation in Energy-Harvesting-Based Wireless Networks

In energy-limited networks, battery-powered nodes suffer from energy famine, which can reduce network lifetime and affect the robustness of networks. To alleviate an energy problem, it is possible to harvest energy from ambient radio frequency signals. In this paper, we consider a proportional fair energy efficiency, which jointly considers energy efficiency and fairness in energy-harvesting-based wireless networks. We formulate a nonconvex optimization problem for solving subchannel and power allocation in order to maximize proportional fair energy efficiency. Using nonlinear fractional programming, we transform the optimization problem into a tractable convex problem. We also derive the solution of the transformed problem and propose a resource allocation algorithm using an iterative method. In addition, we prove the convergence of the proposed algorithm in view of a suboptimal point. Through intensive simulations, we compare the performance of our proposed algorithm with those of conventional algorithms. It is shown that the proposed algorithm improves fairness considerably while maintaining energy efficiency, compared with conventional algorithms.

Multiband Ambient RF Energy Harvesting Circuit Design for Enabling Batteryless Sensors and IoT

Ambient radio frequency (RF) energy harvesting (RF-EH) allows powering low-power electronic devices without wires, batteries, and dedicated energy sources. Current RF-EH circuit designs for ambient RF harvesting are optimized and fabricated for a predetermined frequency band. Thus, a single circuit is tuned for a given band with simple extensions to multiple circuits operating individually in distinct bands. Our approach is different in the sense that it designs and implements a common circuit design that can operate on multiple different RF cellular and ISM bands. This paper makes two contributions. First, it presents a study of ambient RF signal strength distribution conducted in Boston, MA, USA, indicating locations and associated RF bands that can point toward the practicality of ambient RF-EH. Second, it demonstrates an adjustable circuit for harvesting from LTE 700-MHz, GSM 850-MHz, and ISM 900-MHz bands with one single circuit. Our circuit design is fabricated on printed circuit board with comprehensive evaluations at each associated frequency to test the power conversion efficiency (PCE). In addition, we characterize the charging performance, and feasibility of powering sensors outdoors such as TI eZ430-RF2500. Results reveal more than 45% PCE for our prototype.

Energy Efficiency Maximization in Mobile Wireless Energy Harvesting Sensor Networks

In mobile wireless sensor networks (MWSNs), scavenging energy from ambient radio frequency (RF) signals is a promising solution to prolonging the lifetime of energy-constrained relay nodes. In this paper, we apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique to a MWSN where the energy harvested by relay nodes can compensate their energy consumption on data forwarding. In such a network, how to maximize system energy efficiency (bits/Joule delivered to relays) by trading off energy harvesting and data forwarding is a critical issue. To this end, we design a resource allocation (ResAll) algorithm by considering different power splitting abilities of relays under two scenarios. In the first scenario, the power received by relays is split into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, the received power is only split into a discrete set of power streams with fixed power splitting ratios. For each scenario above, we formulate the ResAll problem in a MWSN with SWIPT as a non-convex energy efficiency maximization problem. By exploiting fractional programming and dual decomposition, we further propose a cross-layer ResAll algorithm consisting of subalgorithms for rate control, power allocation, and power splitting to solve the problem efficiently and optimally. Simulation results reveal that the proposed ResAll algorithm converges within a small number of iterations, and achieves optimal system energy efficiency by balancing energy efficiency, data rate, transmit power, and power splitting ratio.

Symbol Detection of Ambient Backscatter Systems With Manchester Coding

Ambient backscatter communication is a newly emerged paradigm, which utilizes the ambient radio frequency signal as the carrier to reduce the system battery requirement, and is regarded as a promising solution for enabling large-scale deployment of future Internet of Things networks. The key issue of ambient backscatter communication systems is how to perform reliable detection. In this paper, we propose novel encoding methods at the information tag and devise the corresponding symbol detection methods at the reader. In particular, Manchester coding and differential Manchester coding are adopted at the information tag, and the corresponding semi-coherent Manchester (SeCoMC) and non-coherent Manchester (NoCoMC) detectors are developed. In addition, analytical bit-error-rate (BER) expressions are characterized for both detectors assuming either complex Gaussian or unknown deterministic ambient signal. Simulation results show that the BER performance of unknown deterministic ambient signal is better, and the SeCoMC detector outperforms the NoCoMC detector. Finally, compared with the prior detectors for ambient backscatter communications, the proposed detectors have the advantages of achieving superior BER performance with lower communication delay.

Detection, Tracking, and Interdiction for Amateur Drones

Unmanned aerial vehicles, also known as drones, are expected to play major roles in future smart cities, for example, by delivering goods and merchandise, serving as mobile hotspots for broadband wireless access, and maintaining surveillance and security. The goal of this survey article is to study various potential cyber and physical threats that may arise from the use of UAVs, and subsequently review various ways to detect, track, and interdict malicious drones. In particular, we review techniques that rely on ambient radio frequency signals (emitted from UAVs), radars, acoustic sensors, and computer vision techniques for detection of malicious UAVs. We present some early experimental and simulation results on range estimation of UAVs and receding horizon tracking of UAVs. Finally, we summarize common techniques that are considered for interdiction of UAVs.

Wireless Energy Transfer Enabled D2D in Underlaying Cellular Networks

Wireless powered device-to-device (D2D) communications can prolong the lifetime of energy constrained D2D transmitters. This paper proposes an analytical model for D2D communications powered by both ambient radio frequency (RF) signals from base stations (BSs) and power beacons (PBs) in a multichannel downlink cellular network to enhance harvested energy at D2D transmitters without changing the original cellular networks. With fixed transmit powers of D2D transmitters, we derive a probability for D2D transmitters to harvest enough energy for D2D communications based on stochastic geometry. We investigate the profit gained by a cellular system with RF wireless energy transfer enabled D2D (WET-D2D), where the profit is defined as the difference between the revenue earned from downlink data transmission from BSs and D2D communications and the energy cost for BSs and PBs. Numerical results verify that a maximum profit of a cellular system with WET-D2D is achieved with a well-designed PB and proper transmit powers at D2D transmitters.

Secrecy Rate Optimization for Cooperative Cognitive Radio Networks Aided by a Wireless Energy Harvesting Jammer

In this paper, a novel wireless energy harvesting cooperative jammer (EH-CJ)-aided transmission scheme, which can enhance the security for cooperative cognitive radio networks (CCRNs), is proposed. The EH-CJ first harvests energy from ambient radio frequency (RF) signals transmitted from the primary transmitter (PT). This energy is then used to transmit jamming noise (JN), so that the performance of eavesdroppers’ receptions is degraded, and also to deliver additional power to the secondary energy receiver (ER). In this way, the proposed scheme can enhance the security and extend the lifetime for CCRNs. For this system, we formulate an optimization problem for maximizing the secrecy rate of the secondary system under transmit power constraints at the secondary transmitter as well as the EH-CJ, minimum harvested energy at the secondary ER and information rate at the primary receiver (PR). Since this optimization problem is not convex, we propose a two-stage optimization algorithm based upon the Charnes–Cooper transformation and rank-one relaxation to solve it. Computer simulation results have demonstrated that the proposed EH-CJ-aided scheme achieves better secrecy rate performance, as compared to an isotropic JN scheme as well as compared to an equivalent scheme which does not use the aid of EH-CJ.

Ambient Backscatter Communications: A Contemporary Survey

Recently, ambient backscatter communication has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks, and thus it is expected to realize numerous Internet-of-Things applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.

Signal Ratio Detection and Approximate Performance Analysis for Ambient Backscatter Communication Systems with Multiple Receiving Antennas

Recently, ambient backscatter attracts much attention since it can utilize ambient radio frequency signals to enable battery-free devices to communicate with others. Most existing studies about ambient backscatter assume that the reader is equipped with one receiving antenna. In practice, the reader can utilize multiple antennas to overcome channel fading. In this paper, we investigate the problem of signal detection for ambient backscatter systems with multiple receiving antennas. Specifically, we formulate a new transmission model where the reader is equipped with at least two antennas and propose a ratio detector that exploits the ratio of the signal strength received at each antenna. It is shown that the closed-form expression of the optimal detection threshold for this detector is difficult to derive. Therefore, we derive a reasonable approximate expression for the optimal detection threshold. Moreover, we obtain the closed-form expression for approximate bit error rate (BER). Furthermore, we propose an antenna selection scheme if the reader is equipped with more than two antennas. The selection scheme is investigated through the BER performance. It is found that the largest gain in BER can be achieved when the antenna number increases from two to three, and that much less gain is obtained from enlarging the antenna number when the reader already has four antennas. Finally, simulation results are provided to corroborate our theoretical studies.

Optimizing Joint Data and Power Transfer in Energy Harvesting Multiuser Wireless Networks

Energy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on Radio Frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. Specifically, we propose joint data and energy transfer optimization frameworks for powering mobile wireless devices through RF energy harvesting. We introduce a power utility that captures the power consumption cost at the base station (BS) and the used power from the users' batteries, and determine optimal power resource allocations that meet data rate requirements of downlink and uplink communications. Two types of harvesting capabilities are considered at each user: harvesting only from dedicated RF signals and hybrid harvesting from both dedicated and ambient RF signals. The developed frameworks increase the end users' battery lifetime at the cost of a slight increase in the BS power consumption. Several evaluation studies are conducted in order to validate our proposed frameworks.

An Overview of Sustainable Green 5G Networks

The stringent requirements of a 1000x increase in data traffic and 1 ms round-trip latency have made limiting the potentially tremendous ensuing energy consumption one of the most challenging problems for the design of the upcoming 5G networks. To enable sustainable 5G networks, new technologies have been proposed to improve the system energy efficiency, and alternative energy sources are introduced to reduce our dependence on traditional fossil fuels. In particular, various 5G techniques target the reduction of the energy consumption without sacrificing the quality of service. Meanwhile, energy harvesting technologies, which enable communication transceivers to harvest energy from various renewable resources and ambient radio frequency signals for communication, have drawn significant interest from both academia and industry. In this article, we provide an overview of the latest research on both green 5G techniques and energy harvesting for communication. In addition, some technical challenges and potential research topics for realizing sustainable green 5G networks are also identified.