Simultaneous wireless information and power transfer (SWIPT) in cooperative networks

2019 Spring.Includes bibliographical references.In recent years, the capacity and charging speed of batteries have become the bottleneck of mobile communications systems. Energy harvesting (EH) is regarded as a promising technology to significantly extend the lifetime of battery-powered devices. Among many EH technologies, simultaneous wireless information and power transfer (SWIPT) proposes to harvest part of the energy carried by the wireless communication signals. In particular, SWIPT has been successfully applied to energy-constrained relays that are mainly or exclusively powered by the energy harvested from the received signals. These relays are known as EH relays, which attract significant attention in both the academia and the industry. In this research, we investigate the performance of SWIPT-based EH cooperative networks and the optimization problems therein. Due to hardware limitations, the energy harvesting circuit cannot decode the signal directly. Power splitting (PS) is a popular and effective solution to this problem. Therefore, we focus on PS based SWIPT in this research. First, different from existing work that employs time-switching (TS) based SWIPT, we propose to employ PS based SWIPT for a truly full-duplex (FD) EH relay network, where the information reception and transmission take place simultaneously at the relay all the time. This more thorough exploitation of the FD feature consequently leads to a significant capacity improvement compared with existing alternatives in the literature. Secondly, when multiple relays are available in the network, we explore the relay selection (RS) and network beamforming techniques in EH relay networks. Assuming orthogonal bandwidth allocation, both single relay selection (SRS) and general relay selection (GRS) without the limit on the number of cooperating relays are investigated and the corresponding RS methods are proposed. We will show that our proposed heuristic GRS methods outperform the SRS methods and achieve very similar performance compared with the optimal RS method achieved by exhaustive search but with dramatically reduced complexity. Under the shared bandwidth assumption, network beamforming among EH relays is investigated. We propose a joint PS factor optimization method based on semidefinite relaxation. Simulations show that network beamforming achieves the best performance among all other cooperative techniques. Finally, we study the problem of power allocation and PS factor optimization for SWIPT over doubly-selective wireless channels. In contrast to existing work in the literature, we take the channel variation in both time and frequency domains into consideration and jointly optimize the power allocation and the PS factors. The objective is to maximize the achievable data rate with constraints on the delivered energy in a time window. Since the problem is difficult to solve directly due to its nonconvexity, we proposed a two-step approach, named joint power allocation and splitting (JoPAS), to solve the problem along the time and frequency dimensions sequentially. Simulations show significantly improved performance compared with the existing dynamic power splitting scheme. A suboptimal heuristic algorithm, named decoupled power allocation and splitting (DePAS), is also proposed with significantly reduced computational complexity and simulations demonstrate its near-optimum performance

AIRS-assisted Vehicular Networks with Rate-Splitting SWIPT Receivers: Joint Trajectory and Communication Design

In this correspondence, we propose to use an intelligent reflective surface (IRS) installed on unmanned aerial vehicle (UAV), referred to as aerial IRS (AIRS), for vehicular networks, where simultaneous wireless information and power transfer (SWIPT) receivers to concurrently allow information decoding (ID) and energy harvesting (EH) are equipped at the battery-limited vehicles. For efficiently supporting the multiple moving vehicles, we adopt rate-splitting multiple access (RSMA) technique. With the aim of maximizing the sum rate of vehicles, we jointly optimize trajectory and phase shift design of AIRS, transmit power and rate allocation for RSMA along with power splitting ratio for SWIPT implementation. Via simulations, the superior performances of the proposed algorithm are validated compared to the conventional partial optimizations.Comment: 6 pages, 5 figure

Adaptive Resource Allocation Algorithms For Data And Energy Integrated Networks Supporting Internet of Things

According to the forecast, there are around 2.1 billion IoT devices connected to the network by 2022. The rapidly increased IoT devices bring enormous pressure to the energy management work as most of them are battery-powered gadgets. What’s more, in some specific scenarios, the IoT nodes are fitted in some extreme environment. For example, a large-scale IoT pressure sensor system is deployed underneath the floor to detect people moving across the floor. A density-viscosity sensor is deployed inside the fermenting vat to discriminate variations in density and viscosity for monitoring the wine fermentation. A strain distribution wireless sensor for detecting the crack formation of the bridge is deployed underneath the bridge and attached near the welded part of the steel. It is difficult for people to have an access to the extreme environment. Hence, the energy management work, namely, replacing batteries for the rapidly increased IoT sensors in the extreme environment brings more challenges. In order to reduce the frequency of changing batteries, the thesis proposes a self-management Data and Energy Integrated Network (DEIN) system, which designs a stable and controllable ambient RF resource to charge the battery-less IoT wireless devices. It embraces an adaptive energy management mechanism for automatically maintaining the energy level of the battery-less IoT wireless devices, which always keeps the devices within a workable voltage range that is from 2.9 to 4.0 volts. Based on the DEIN system, RF energy transmission is achieved by transmitting the designed packets with enhanced transmission power. However, it partly occupies the bandwidth which was only used for wireless information transmission. Hence, a scheduling cycle mechanism is proposed in the thesis for organizing the RF energy and wireless information transmission in separate time slots. In addition, a bandwidth allocation algorithm is proposed to minimize the bandwidth for RF energy transmission in order to maximize the throughput of wireless information. To harvest the RF energy, the RF-to-DC energy conversion is essential at the receiver side. According to the existing technologies, the hardware design of the RF-to-DC energy converter is normally realized by the voltage rectifier which is structured by multiple Schottky diodes and capacitors. Research proves that a maximum of 84% RF-to-DC conversion efficiency is obtained by comparing a variety of different wireless band for transmitting RF energy. Furthermore, there is energy loss in the air during transmitting the RF energy to the receiver. Moreover, the circuital loss happens when the harvested energy is utilized by electronic components. Hence, how to improve the efficiency of RF energy utilization is considered in the thesis. According to the scenario proposed in the thesis, the harvested energy is mainly consumed for uplink transmission. a resource allocation algorithm is proposed to minimize the system’s energy consumption per bit of uplink data. It works out the optimal transmission power for RF energy as well as the bandwidth allocated for RF energy and wireless information transmission. Referring to the existing RF energy transmission and harvesting application on the market, the Powercast uses the supercapacitor to preserve the harvested RF energy. Due to the lack of self-control energy management mechanism for the embedded sensor, the harvested energy is consumed quickly, and the system has to keep transmitting RF energy. Existing jobs have proposed energy-saving methods for IoT wireless devices such as how to put them in sleep mode and how to reduce transmission power. However,they are not adaptive, and that would be an issue for a practical application. In the thesis, an energy-saving algorithm is designed to adaptively manage the transmission power of the device for uplink data transmission. The algorithm balances the trade-off between the transmission power and the packet loss rate. It finds the optimal transmission power to minimize the average energy cost for uplink data transmission, which saves the harvested energy to reduce the frequency of RF energy transmission to free more bandwidth for wireless information