Wireless Information and Power Transfer in Full-Duplex Systems with Massive Antenna Arrays

We consider a multiuser wireless system with a full-duplex hybrid access point (HAP) that transmits to a set of users in the downlink channel, while receiving data from a set of energy-constrained sensors in the uplink channel. We assume that the HAP is equipped with a massive antenna array, while all users and sensor nodes have a single antenna. We adopt a time-switching protocol where in the first phase, sensors are powered through wireless energy transfer from HAP and HAP estimates the downlink channel of the users. In the second phase, sensors use the harvested energy to transmit to the HAP. The downlink-uplink sum-rate region is obtained by solving downlink sum-rate maximization problem under a constraint on uplink sum-rate. Moreover, assuming perfect and imperfect channel state information, we derive expressions for the achievable uplink and downlink rates in the large-antenna limit and approximate results that hold for any finite number of antennas. Based on these analytical results, we obtain the power-scaling law and analyze the effect of the number of antennas on the cancellation of intra-user interference and the self-interference.Comment: Accepted for the IEEE International Conference on Communications (ICC 2017

INVESTIGATING TECHNIQUES AND RESEARCH TRENDS IN RF ENERGY HARVESTING

ABSTRACT For more than two decades, there were extensive research activities towards harnessing the energy captured from the natural resources and use it for human utility. This field of research, commonly known as energy harvesting, has various characteristics and subsidiary applications. Presently, it is seen that use of mobile phones are increasing due to the advancement of communication systems and numerous services incorporated within it make the availability of free RF signals. This paper discusses various techniques and attempts initiated by research community in harnessing this free RF signals for the purpose of energy harvesting to power up low-powered electronic devices and embedded systems. It also presents some of the most significant research procedures that use RF signals for harvesting energy and their implausible research gap

From serendipity to sustainable Green IoT: technical, industrial and political perspective

Recently, Internet of Things (IoT) has become one of the largest electronics market for hardware production due to its fast evolving application space. However, one of the key challenges for IoT hardware is the energy efficiency as most of IoT devices/objects are expected to run on batteries for months/years without a battery replacement or on harvested energy sources. Widespread use of IoT has also led to a largescale rise in the carbon footprint. In this regard, academia, industry and policy-makers are constantly working towards new energy-efficient hardware and software solutions paving the way for an emerging area referred to as green-IoT. With the direct integration and the evolution of smart communication between physical world and computer-based systems, IoT devices are also expected to reduce the total amount of energy consumption for the Information and Communication Technologies (ICT) sector. However, in order to increase its chance of success and to help at reducing the overall energy consumption and carbon emissions a comprehensive investigation into how to achieve green-IoT is required. In this context, this paper surveys the green perspective of the IoT paradigm and aims to contribute at establishing a global approach for green-IoT environments. A comprehensive approach is presented that focuses not only on the specific solutions but also on the interaction among them, and highlights the precautions/decisions the policy makers need to take. On one side, the ongoing European projects and standardization efforts as well as industry and academia based solutions are presented and on the other side, the challenges, open issues, lessons learned and the role of policymakers towards green-IoT are discussed. The survey shows that due to many existing open issues (e.g., technical considerations, lack of standardization, security and privacy, governance and legislation, etc.) that still need to be addressed, a realistic implementation of a sustainable green-IoT environment that could be universally accepted and deployed, is still missing

Waveform and Beamforming Design for Intelligent Reflecting Surface Aided Wireless Power Transfer: Single-User and Multi-User Solutions

In this paper, we study the waveform and passive beamforming design for intelligent reflecting surface (IRS)-aided wireless power transfer (WPT). Generalized multi-user and low complexity single-user algorithms are derived based on alternating optimization (AO) framework to maximize the weighted sum output DC current, subject to transmit power constraints and passive beamforming phases unit modulus constraints. The input signal waveform and IRS passive beamforming phase shifts are jointly designed as a function of users' individual frequency-selective channel state information (CSI). The energy harvester nonlinearity is explored and two IRS deployment schemes, namely frequency selective IRS (FS-IRS) and frequency flat IRS (FF-IRS), are modeled and analyzed. This paper highlights the fact that IRS can provide an extra passive beamforming gain on output DC power over conventional WPT designs and significantly influence the waveform design by leveraging the benefit of passive beamforming, frequency diversity and energy harvester nonlinearity. Even though FF-IRS exhibits lower output DC current than FS-IRS, it still achieves substantially increased DC power over conventional WPT designs. Performance evaluations confirm the significant benefits of a joint waveform and passive beamforming design accounting for the energy harvester nonlinearity to boost the performance of single-user and multi-user WPT system.Comment: 32 pages, 19 figures, submitted for publicatio

A self-powered single-chip wireless sensor platform

Internet of things” require a large array of low-cost sensor nodes, wireless connectivity, low power operation and system intelligence. On the other hand, wireless biomedical implants demand additional specifications including small form factor, a choice of wireless operating frequencies within the window for minimum tissue loss and bio-compatibility This thesis describes a low power and low-cost internet of things system suitable for implant applications that is implemented in its entirety on a single standard CMOS chip with an area smaller than 0.5 mm2. The chip includes integrated sensors, ultra-low-power transceivers, and additional interface and digital control electronics while it does not require a battery or complex packaging schemes. It is powered through electromagnetic (EM) radiation using its on-chip miniature antenna that also assists with transmit and receive functions. The chip can operate at a short distance (a few centimeters) from an EM source that also serves as its wireless link. Design methodology, system simulation and optimization and early measurement results are presented

Microwave antennas for infrastructure health monitoring

Infrastructure health monitoring (IHM) is a technology that has been developed for the detection and evaluation of changes that affect the performance of built infrastructure systems such as bridges and buildings. One of the employed methods for IHM is wireless sensors method which is based on sensors embedded in concrete or mounted on surface of structure during or after the construction to collect and report valuable monitoring data such as temperature, displacement, pressure, strain and moisture content, and information about defects such as cracks, voids, honeycombs, impact damages and delamination. The data and information can then be used to access the health of a structure during and/or after construction. Wireless embedded sensor technique is also a promising solution for decreasing the high installation and maintenance cost of the conventional wire based monitoring systems. However, several issues should be resolved at research and development stage in order to apply them widely in practice. One of these issues is that wireless sensors cannot operate for a long time due to limited lifetime of batteries. Once the sensors are embedded within a structure, they may not be easily accessible physically without damaging the structure. The main aim of this research is to develop effective antennas for IHM applications such as detection of defects such as gaps representing cracks and delaminations, and wireless powering of embeddable sensors or recharging their batteries. For this purpose, modelling of antennas based on conventional antipodal Vivaldi antennas (CAVA) and parametric studies are performed using a computational tool CST Studio (Studio 2015) including CST Microwave Studio and CST Design Studio, and experimental measurements are conducted using a performance network analyser. Firstly, modified antipodal Vivaldi antenna (MAVA) at frequency range of 0.65 GHz – 6 GHz is designed and applied for numerical and experimental investigations of the reflection and transmission properties of concrete-based samples possessing air gap or rebars. The results of gap detection demonstrate ability of the developed MAVA for detection of air gaps and delivery of power to embeddable antennas and sensors placed at any depth inside 350-mm thick concrete samples. The investigation into the influence of rebars show that the rebar cell can act as a shield for microwaves if mesh period parameter is less than the electrical half wavelength. At higher frequencies of the frequency range, microwaves can penetrate through the reinforced concrete samples. These results are used for the investigating the transmission of microwaves at the single frequency of 2.45 GHz between the MAVA and a microstrip patch antenna embedded inside reinforced concrete samples at the location of the rebar cell. It is shown that -15 dB coupling between the antennas can be achieved for the samples with rebar cell parameters used in practice. Secondly, a relatively small and high-gain resonant antipodal Vivaldi antenna (RAVA) as a transmitting antenna and modified microstrip patch antenna as an embeddable receiving antenna are designed to operate at 2.45 GHz for powering the sensors or recharging their batteries embedded in reinforced concrete members. These members included reinforced dry and saturated concrete slabs and columns with different values of mesh period of rebars and steel ratio, respectively. Parametric study on the most critical parameters, which affect electromagnetic (EM) wave propagation in these members, is performed. It is shown that there is a critical value of mesh period of rebars with respect to reflection and transmission properties of the slabs, which is related to a half wavelength in concrete. The maximum coupling between antennas can be achieved at this value. The investigation into reinforced concrete columns demonstrates that polarisation configuration of the two-antenna setup with respect to rebars and steel ratios as well as losses in concrete are important parameters. It is observed that the coupling between the antennas reduces faster by increasing the value of steel ratio in parallel than in vertical configuration due to the increase of the interaction between electromagnetic waves and the rebars. This effect is more pronounced in the saturated than in dry reinforced concrete columns. Finally, a relatively high gain 4-element RAVA array with a Wilkinson power divider, feeding network and an embeddable rectenna consisting of the microstrip patch antenna and a rectified circuit are developed. Two wireless power transmission systems, one with a single RAVA and another with the RAVA array, are designed for recharging batteries of sensors embedded inside reinforced concrete slabs and columns with different configurations and moisture content. Comparison between these systems shows that the DC output voltage for recharging commonly used batteries can be provided by the systems with the single RAVA and the system with the RAVA array at the distance between the transmitting antenna and the surface of reinforced concrete members of 0.12 m and 0.6 m, respectively, i.e. the distance achieved when the array is 5 times longer that the distance achieved with a single antenna

Machine learning and energy efficient cognitive radio

With an explosion of wireless mobile devices and services, system designers are facing a challenge of spectrum scarcity and high energy consumption. Cognitive radio (CR) is a promising solution for fulfilling the growing demand of radio spectrum using dynamic spectrum access. It has the ability of sensing, allocating, sharing and adapting to the radio environment. In this thesis, an analytical performance evaluation of the machine learning and energy efficient cognitive radio systems has been investigated while taking some realistic conditions into account. Firstly, bio-inspired techniques, including re y algorithm (FFA), fish school search (FSS) and particle swarm optimization (PSO), have been utilized in this thesis to evaluate the optimal weighting vectors for cooperative spectrum sensing (CSS) and spectrum allocation in the cognitive radio systems. This evaluation is performed for more realistic signals that suffer from the non-linear distortions, caused by the power amplifiers. The thesis then takes the investigation further by analysing the spectrum occupancy in the cognitive radio systems using different machine learning techniques. Four machine learning algorithms, including naive bayesian classifier (NBC), decision trees (DT), support vector machine (SVM) and hidden markov model (HMM) have been studied to find the best technique with the highest classification accuracy (CA). A detailed comparison of the supervised and unsupervised algorithms in terms of the computational time and classification accuracy has been presented. In addition to this, the thesis investigates the energy efficient cognitive radio systems because energy harvesting enables the perpetual operation of the wireless networks without the need of battery change. In particular, energy can be harvested from the radio waves in the radio frequency spectrum. For ensuring reliable performance, energy prediction has been proposed as a key component for optimizing the energy harvesting because it equips the harvesting nodes with adaptation to the energy availability. Two machine learning techniques, linear regression (LR) and decision trees (DT) have been utilized to predict the harvested energy using real-time power measurements in the radio spectrum. Furthermore, the conventional energy harvesting cognitive radios do not assume any energy harvesting capability at the primary users (PUs). However, this is not the case when primary users are wirelessly powered. In this thesis, a novel framework has been proposed where PUs possess the energy harvesting capabilities and can get benefit from the presence of the secondary user (SU) without any predetermined agreement. The performances of the wireless powered PUs and the SU has also been analysed. Numerical results have been presented to show the accuracy of the analysis. First, it has been observed that bio-inspired techniques outperform the conventional algorithms used for collaborative spectrum sensing and allocation. Second, it has been noticed that SVM is the best algorithm among all the supervised and unsupervised classifiers. Based on this, a new SVM algorithm has been proposed by combining SVM with FFA. It has also been observed that SVM+FFA outperform all other machine leaning classifiers Third, it has been noticed in the energy predictive modelling framework that LR outperforms DT by achieving smaller prediction error. It has also been shown that optimal time and frequency attained using energy predictive model can be used for defining the scheduling policies of the harvesting nodes. Last, it has been shown that wirelessly powered PUs having energy harvesting capabilities can attain energy gain from the transmission of SU and SU can attain the throughput gain from the extra transmission time allocated for energy harvesting PUs

Anechoic and reverberation chamber design and measurements

Two different chambers are studied in this thesis: the anechoic chamber (AC) and the reverberation chamber (RC). The AC has been developed for many years, while in the past few years RC has emerged as a promising facility not just in electromagnetic compatibility (EMC) measurements but also as a multi-disciplinary research facility in various areas. For the anechoic chamber, a CAD tool is developed to aid the design of an anechoic chamber. The objective is to estimate the chamber performance accurately. The ultimate goal is to minimise the cost but optimise the chamber performance for given conditions and specifications. This is very important for a commercial company. The CAD tool is developed based on the GO theory, two different algorithms are realised, acceleration techniques are applied, and measurements are performed to validate the CAD tool. For the reverberation chamber, a series of new measurement methods in the RC are developed including antenna radiation efficiency measurement, diversity gain measurement, radiated emission measurement, material characterisation, shielding effectiveness, volume measurement, etc. Finally, we apply the B-scan in an RC to characterise the behaviour of the electric field in the time domain. Statistical characterisation of the electric field in the time domain is given, stirrer efficiency is quantified based on the total scattering cross section (TSCS) of stirrers, and time gating technique in the RC is introduced. It has been found that the stirrer efficiency can be well-quantified in the time domain and the definition of stirrer efficiency in this thesis provides a universal and quantitative way to compare the performance between different RCs or different stirrer designs in one RC