Initial efforts on wireless power transfer (WPT) have concentrated toward long-distance transmission and high power applications. Nonetheless, the lower achievable transmission efficiency and potential health concerns arising due to high power applications, have caused limitations in their further developments. Due to tremendous energy consumption growth with ever-increasing connected devices, alternative wireless information and power transfer techniques have been important not only for theoretical research but also for the operational costs saving and for the sustainable growth of wireless communications. In this regard, radio frequency energy harvesting (RF-EH) for a wireless communications system presents a new paradigm that allows wireless nodes to recharge their batteries from the RF signals instead of fixed power grids and the traditional energy sources. In this approach, the RF energy is harvested from ambient electromagnetic sources or from the sources that directionally transmit RF energy for EH purposes. Notable research activities and major advances have occurred over the last decade in this direction. Thus, this paper provides a comprehensive survey of the state-of-art techniques, based on advances and open issues presented by simultaneous wireless information and power transfer (SWIPT) and WPT assisted technologies. More specifically, in contrast to the existing works, this paper identifies and provides a detailed description of various potential emerging technologies for the fifth generation communications with SWIPT/WPT. Moreover, we provide some interesting research challenges and recommendations with the objective of stimulating future research in this emerging domain.

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Simultaneous Wireless Information and Power Transfer (SWIPT): Recent Advances and Future Challenges

In this paper, a hybrid visible light communication-radio frequency (RF) system, including a legitimate receiver (R) and an eavesdropper (E) is considered R can harvest energy from the light emitted by light emitting diodes (LEDs), which is used for the information transmission between R and the RF receiver which is close to the LED It is assumed that E tries to eavesdrop the information delivered from R to the RF receiver and R is with finite energy storage Considering the randomness of the locations of R and E, the statistical characteristics of the received signal-to-noise ratio at the RF receiver and E are characterized; then, we derive the analytical expressions for exact and asymptotic secrecy outage probability by using the stochastic geometry method Finally, simulations are carried out to verify our proposed analytical models

Secure Hybrid VLC-RF Systems With Light Energy Harvesting

5G-based green broadband communication system design with simultaneous wireless information and power transfer

In this paper, we consider an underlay multiple-input-multiple-output (MIMO) cognitive radio network (CRN) including a pair of primary nodes, a couple of secondary nodes, and an eavesdropper, where the secondary transmitter is powered by the renewable energy harvested from the primary transmitter in order to improve both energy efficiency and spectral efficiency. Based on whether the channel state information of wiretap links are available or not, the secrecy outage performance of the optimal antenna selection (OAS) scheme and suboptimal antenna selection (SAS) scheme for underlay MIMO CRN with energy harvesting are investigated and compared with traditional space-time transmission scheme. The closed-form expressions for exact and asymptotic secrecy outage probability are derived. Monte-Carlo simulations are conducted to testify the accuracy of the analytical results. The analysis illustrates that the OAS scheme outperforms SAS scheme. Furthermore, the asymptotic result shows that no matter which scheme is considered, the OAS and SAS schemes can achieve the same secrecy diversity order.

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On Secure Underlay MIMO Cognitive Radio Networks With Energy Harvesting and Transmit Antenna Selection

This paper investigates and optimizes the performance of simultaneous wireless information and power transfer (SWIPT) in wireless sensor networks over Nakagami- $m$ fading channels. In the considered system, there is one mobile reader ( $R$ ), which is equipped with one transmit antenna and one receive antenna, and a group of passive sensors. The information delivery includes two stages: 1) $R$ broadcasts a command with radio-frequency energy to the sensors, which adopt time splitting (TS)/power splitting (PS) schemes to harvest energy and 2) sensors deliver their information to $R$ over orthogonal channels by using the harvested energy. In this paper, we propose a unified framework to study and optimize the impact of SWIPT on the system performance with both TS and PS schemes. First, we characterize the probability density function and cumulative distribution function of the signal-to-interference-plus-noise-ratio in high signal-to-noise ratio region, then we study the outage and ergodic capacity performance of the backward links. The approximated closed-form expressions for the outage probability and ergodic capacity are derived and validated through Monte Carlo simulations. Finally, we also evaluate the energy efficiency of the target system, and propose an optimal splitting scheme for TS and PS to maximize the throughput of the target system.

Performance Analysis and Optimization for SWIPT Wireless Sensor Networks

In this paper, a multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) system, including one base station (BS) equipped with multiple antennas, one desired single-antenna information receiver (IR), and $N$ ( $N > 1$ ) single-antenna energy-harvesting receivers (ERs) is considered. Assuming that the information signal to the desired IR may be eavesdropped by ERs if ERs are malicious, we investigate the secrecy performance of the target MISO SWIPT system when imperfect channel state information (CSI) is available and adopted for transmit antenna selection at the BS. Considering that each eavesdropping link experiences independent but not necessarily identically distributed Rayleigh fading, the closed-form expressions for the exact and the asymptotic secrecy outage probability, and the average secrecy capacity are derived and verified by simulations. Furthermore, the optimal power splitting factor is derived for each ER to realize the tradeoff between the energy harvesting and the information eavesdropping. Our results reveal the impact of the imperfect CSI on the secrecy performance of MISO SWIPT systems in the presence of multiple wiretap channels.

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On Secrecy Performance of MISO SWIPT Systems With TAS and Imperfect CSI

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments.

Learning-Based Signal Detection for MIMO Systems With Unknown Noise Statistics

This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments. The code of this paper is available at this https URL.

Lisheng Fan

Papers

On Secrecy Performance of MISO SWIPT Systems With TAS and Imperfect CSI

Learning-Based Signal Detection for MIMO Systems With Unknown Noise Statistics

Learning based signal detection for MIMO systems with unknown noise statistics