Multiuser MIMO Wireless Energy Transfer With Coexisting Opportunistic Communication

PurposeThis paper aims to develop mathematical models of variously compensated wireless energy transfer (WET) systems. Attention is primarily paid to the derivation of the most important energy transfer characteristics such as efficiency and amount of transferred power. This paper discusses the main advantages and disadvantages of various compensation techniques to show their possible application areas. On the basis of these results, a designer will be able to quickly identify which compensation type suites as the best solution to fulfill a given system’s requirements.Design/methodology/approachFirst, the current state in the field of mathematical modeling of WET systems is introduced. Next, the non-resonant magnetic-coupled circuit together with four most common resonant magnetic-coupled circuits is analyzed. The equivalent circuit models using loop currents methodology is applied to the analyses. The proposed methodology is experimentally verified by the laboratory measurement of selected circuit topology. The main contribution of the proposed methodology lies in its quick applicability on more complicated or extended systems while keeping a relatively good match with the real system’s behavior.FindingsThe authors have presented the usage of a simple and accurate methodology for investigating variously compensated WET systems. Electrical engineers who require effective and powerful tools for the identification of basic WET systems properties will find this methodology to be of extensive help.Research limitations/implicationsThe analyses consider only the sinusoidal type of supply voltage; so, it is valid mainly for the close range of the resonant state. Nonlinearities cannot be taken into account.Practical implicationsThis research may be applied in the field of WET systems.Originality/valueResearch in the area of power electronic systems, which provides a clear and straightforward procedure for WET system identification, will be helpful to most practical technicians who are not well versed in areas of physical-based phenomena.

Wireless energy transfer (WET) is potentially a promising solution to provide convenient and reliable energy supplies for energy-constrained networks, and has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming, is an essential technique for WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the optimal design of one efficient channel-acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system, by exploiting the channel reciprocity based on which the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net energy at the ER, which is the total energy harvested offset by that used for channel training. The optimal training design, including the number of receive antennas to be trained, as well as the training time and power allocated, is derived. Our result shows that training helps only when the following conditions are satisfied: (i) the channel coherence time is sufficiently large; (ii) the number of antennas at the ET is large enough; and (iii) the effective signal-to-noise ratio (ESNR) is sufficiently high; otherwise, no training should be applied and isotropic energy transmission is optimal.

The current trend puts emphasis on the efficiency of the all electronics devices. For this reason, it is necessary to maximize the efficiency of the wireless energy transfer system as well as on other electronic devices. Therefore, this article deals with the most suitable working point analysis of the serial–serial WET system. The core of the article contains the simulation analysis at the various loads and various coupling coefficients. Experimental measurements were made on the existing prototype and the results were compared with the results from the simulation analysis. The main outcome of this paper is thus detailed investigation of operational point of WET system in order to meet the highest possible system efficiency together with the highest possible level of transferred power.

Energy beamforming (EB) is a key technique for achieving efficient radio-frequency (RF) transmission enabled wireless energy transfer (WET). By optimally designing the waveforms from multiple energy transmitters (ETs) over the wireless channels, they are constructively combined at the energy receiver (ER) to achieve an EB gain that scales with the number of ETs. However, the optimal design of transmit waveforms requires accurate channel state information (CSI) at the ETs, which is challenging to obtain in practical WET systems. In this paper, we propose a new channel training scheme to achieve optimal EB gain in a distributed WET system, where multiple separated ETs adjust their transmit phases to collaboratively send power to a single ER in an iterative manner, based on one-bit feedback from the ER per training interval which indicates the increase/decrease of the received power level from one particular ET over two preassigned transmit phases. The proposed EB algorithm can be efficiently implemented in practical WET systems even with a large number of distributed ETs, and is analytically shown to converge quickly to the optimal EB design as the number of feedback intervals per ET increases. Numerical results are provided to evaluate the performance of the proposed algorithm as compared to other distributed EB designs.

Wireless powered communication (WPC) is able to provide wireless devices (WDs) practically infinite energy by using the radio frequency energy harvesting technique. The electricity of WDs for the wireless information transmission (WIT) is converted from the received electromagnetic signals of wireless energy transfer (WET). Power beacon (PB) is usually deployed as the dedicated energy source in WPC networks and the performance of WET is sensitive to the path-loss effect. Thus, both WET and WIT are significantly influenced by the locations of the PBs. To enable efficient transmission in WPC, this paper analytically investigates the impact of the PB deployment strategies on the WPC performance when WDs are clustered around the access points (APs). Specifically, we propose the PB deployment strategy based on the truncated Poisson cluster process (PCP) model. The distribution of PBs is correlated to the locations of WDs by choosing APs as their common parent points. Then, we derive the semi-closed form expressions for the energy outage probability and the transmission success probability. We further present the expressions of the meta distributions of WET and WIT, and investigate the proportion of the WDs in one cluster that achieves successful transmission while satisfying the reliability constraint. Moreover, the distance distribution between the WD and the intra-cluster PBs is analytically derived for the proposed correlated PCP model. By comparing the performance with other deployment strategies, it is shown that the preference of the PB deployment strategy depends on the PB density. The analytical and numerical results can provide insightful guidelines for designing practical WPC systems.

This paper proposes a novel channel estimation method and a cluster-based opportunistic scheduling policy, for a wireless energy transfer (WET) system consisting of multiple low-complex energy receivers (ERs) with limited processing capabilities. Firstly, in the training stage, the energy transmitter (ET) obtains a set of Received Signal Strength Indicator (RSSI) feedback values from all ERs, and these values are used to estimate the channels between the ET and all ERs. Next, based on the channel estimates, the ERs are grouped into clusters, and the cluster that has its members closest to its centroid in phase is selected for dedicated WET. The beamformer that maximizes the minimum harvested energy among all ERs in the selected cluster is found by solving a convex optimization problem. All ERs have the same chance of being selected regardless of their distances from the ET, and hence, this scheduling policy can be considered to be opportunistic as well as fair. It is shown that the proposed method achieves significant performance gains over benchmark schemes.

This chapter provides an overview of the main energy harvesting (EH) technologies and their relevant role in present and future internet of things (IoT) deployments. EH technologies for wireless machine-type devices can be mainly classified into the following two categories: ambient EH, and dedicated EH. The chapter emphasizes on RF–EH, and highlights its advantages compared to competing EH technologies reduced form-factor implementation and native multi-user support. RF–wireless energy transfer (WET) systems consist of three key components: RF transmitters, medium, and EH node. It focuses on the non-decreasing EH transfer function with the incident RF waveform power as sole input. The chapter describes the general architecture of an RF–EH IoT network, and the main layouts for wireless information transfer (WIT) and WET along with potential applications. WET, as a pivotal component of RF–EH networks, is fundamental for realizing the Internet of Energy paradigm.

We study a radio frequency (RF) wireless energy transfer (WET) enabled multiple input multiple output (MIMO) system. A time slotted transmission pattern is considered. Each slot can be divided into two phases, downlink (DL) WET and uplink (UL) wireless information transmission (WIT). Since energy conversion efficiency of the energy harvesting circuits are non-linear, the conventional linear model leads to a mismatch for resource allocation. In this paper, the power allocation algorithm considering the practical non-linear energy harvesting circuits is studied. The optimization problem is formulated to maximize the energy efficiency of system with multiple constraints, i.e., the transmission power, the received power and the minimum harvested energy, which is a non-convex problem. We transform the objective function from fractional form into an equivalent objective function in subtractive form and provide an iterative power allocation algorithm to achieve the optimal solution. Numerical results show that our proposed algorithm with the non-linear RF energy conversion models can achieve much better performance than the algorithm with the conventional linear model.

The effect of various parameters on the resonance-based wireless energy transfer system has been investigated to obtain the design steps for efficient wireless energy transfer. Both theoretically and experimentally, it has been observed that every system has its own strongly coupled region that is an optimum frequency band for which maximum energy transfer occurs. The frequency band of the strongly coupled region changes with the coil size, structure, physical spacing, relative location, and material properties of the wire. Based on the theoretical results, experiments have been carried out to demonstrate the effectiveness and characteristics of the resonant wireless energy transfer method. It is found that the energy transfer efficiency reaches maximum at resonant frequency. The energy transfer efficiency is further enhanced by changing the configuration of coil and through low-resistance wire with high-quality factor. The analyses will give future guidelines for designing a compact efficient wireless energy transfer system to energize macro as well as micro devices used for various applications.

This chapter examines simultaneous wireless information and power transfer (SWIPT) scenarios. It discusses the main SWIPT receivers, namely, separate and co-located receivers. The chapter provides numerical examples on the performance of the designed SWIPT precoding strategy. It focuses on a co-located SWIPT system, where a HAP serves a set S of devices performing both energy harvesting and information decoding in a given frequency band. The chapter illustrates some numerical results on the performance of the time switching and power splitting protocols. It also discusses some specific promising approaches to boost the performance of SWIPT systems for general wireless energy transfer (WET)-powered internet of things networks. WET and SWIPT systems are subject to non-linear responses that are often model-intractable unless too strong assumptions are adopted. The chapter concludes with an overview of some potential techniques to enable efficient SWIPT in future networks.

With simultaneous wireless information and power transfer, the energy of RF signal can be used effectively for the self-sustained operation of wireless devices. In practical applications, power splitting method can be used for simultaneous wireless information and power transmission (SWIPT) between base station and multi-user. Different user systems have different energy consumption, so users with insufficient power need more energy transmission to supplement their power. Nowadays, the beamforming strategy of simultaneous wireless information and energy transmission does not consider the problem of insufficient power at the terminal. This paper proposes a energy state feedback method to optimize simultaneous wireless information and power transmission system. Through the joint design optimization of beamforming and power splitting, the power transmission of users with insufficient power is added, and the minimized transmission power optimization is done under the conditions of satisfying SINR and energy harvesting constraints by utilizing the channel state information and node power information of each node. This optimization model is difficult to solve. In order to solve this problem, the concept of reserve group is introduced. When the optimization model cannot be solved, the users with the worst channel state of the node are put into the reserve group. These users do not participate in the optimization first. After the new channel state information is obtained, the optimization model is established again. If the problem can be solved, the transmission power of base station doesn’t reach the maximum power, some users in the reserve group is added to continue to optimize, if it has reached the maximum power of base station transmission, it is not optimizing, so as to maximize the optimization of all users and improve system performance.

This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number $M$ of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame). To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-$M$ regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system. We define a metric, namely, the massive MIMO degree-of-rate-gain (MM-DoRG), as the asymptotic UL rate normalized by $\log(M)$. We show that the proposed WET-MM system is optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE.

A compact wireless energy transfer scheme for delivering power to the implantable electronic devices has been presented here. The wireless energy transfer system is built by using a printed spiral receiving resonator with a cylindrical source resonator to transfer energy wirelessly through strongly coupled magnetic resonance. Experimentally, it is found that the wireless power transfer efficiency reaches the maximum at the resonant frequency, and the efficiency decreases with the increase of the distance between the source and receiver coils. The proposed design has the significant advantage of being easier to implement for biomedical applications due to sizeable reduction in volume required on the implantable devices.

Accomplishing equilibrium in terms of transfer efficiency for dual-band wireless energy transfer (WET) system remains as one of key concerns particularly in the implementation of a single transmitter device which supports simultaneous energy and data transfer functionality. Three stages of design method are discussed in addressing the aforementioned concern. A single layer dual-band printed spiral resonator for non-radiative wireless energy transfer operating at 6.78 MHz and 13.56 MHz is presented. By employing multi-coil approach, measured power transfer efficiency for a symmetrical link separated at axial distance of 30 mm are 72.34% and 74.02% at the respective frequency bands. When operating distance is varied between 30 mm to 38 mm, consistency of simulated peak transfer efficiency above 50% is achievable.

Intelligent reflecting surface (IRS) can effectively enhance the energy and spectral efficiency of wireless communication system through the use of a large number of lowcost passive reflecting elements. In this paper, we investigate throughput optimization of IRS-assisted user cooperation in a wireless powered communication network (WPCN), where the two WDs harvest wireless energy and transmit information to a common hybrid access point (HAP). In particular, the two WDs first exchange their independent information with each other and then form a virtual antenna array to transmit jointly to the HAP. We aim to maximize the common (minimum) throughput performance by jointly optimizing the transmit time and power allocations of the two WDs on wireless energy and information transmissions and the passive array coefficients on reflecting the wireless energy and information signals. By comparing with some existing benchmark schemes, our results show that the proposed IRS-assisted user cooperation method can effectively improve the throughput performance of cooperative transmission in WPCNs.