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Register a new userBeamforming for Magnetic Induction based Wireless Power Transfer Systems with Multiple Receivers
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Added by
Steven Kisseleff
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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Magnetic induction (MI) based communication and power transfer systems have gained an increased attention in the recent years. Typical applications for these systems lie in the area of wireless charging, near-field communication, and wireless sensor networks. For an optimal system performance, the power efficiency needs to be maximized. Typically, this optimization refers to the impedance matching and tracking of the split-frequencies. However, an important role of magnitude and phase of the input signal has been mostly overlooked. Especially for the wireless power transfer systems with multiple transmitter coils, the optimization of the transmit signals can dramatically improve the power efficiency. In this work, we propose an iterative algorithm for the optimization of the transmit signals for a transmitter with three orthogonal coils and multiple single coil receivers. The proposed scheme significantly outperforms the traditional baseline algorithms in terms of power efficiency.
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Simultaneous wireless information and power transfer (SWIPT) is an appealing research area because both information and energy can be delivered to wireless devices simultaneously. In this paper, we propose a diplexer-based receiver architecture that can utilizes both the doubling frequency and baseband signals after the mixer. The baseband signals are used for information decoding and the doubling frequency signals are converted to direct current for energy harvesting. We analyze the signal in the receiver and find that the power of the energy harvested is equal to that of information decoded. Therefore, the diplexer can be used as a power splitter with a power splitting factor of 0.5. Specifically, we consider a multiuser multi-input single-output (MISO) system, in which each user is equipped with the newly proposed receiver. The problem is formulated as an optimization problem that minimizes the total transmitted power subject to some constraints on each users quality of service and energy harvesting demand. We show that the problem thus formulated is a non-convex quadratically constrained quadratic program (QCQP), which can be solved by semi-definite relaxation.
This paper investigates power splitting for full-duplex relay networks with wireless information and energy transfer. By applying power splitting as a relay transceiver architecture, the full duplex information relaying can be powered by energy harvested from the source-emitted radio frequency signal. In order to minimize outage probability, power splitting ratios have been dynamically optimized according to full channel state information (CSI) and partial CSI, respectively. Under strong loop interference, the proposed full CSI-based and partial CSI-based power splitting schemes achieve the better outage performance than the fixed power splitting scheme, whereas the partial CSI-based power splitting scheme can ensure competitive outage performance without requiring CSI of the second-hop link. It is also observed that the worst outage performance is achieved when the relay is located midway between the source and destination, whereas the outage performance of partial CSI-based power splitting scheme approaches that of full CSI-based scheme when the relay is placed close to the destination.
Radio frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multi-antenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this paper, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as $0.85sqrt{M}$, where $M$ is the number of PBs antenna elements. Moreover, a rotation-specific power control mechanism was proposed to i) fairly optimize the WET process if devices positioning information is available, and/or ii) to avoid hazards to human health in terms of specific absorption rate (SAR). We show that RAB performance even approaches quickly (or surpasses, for scenarios with sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.
With the high development of wireless communication techniques, it is widely used in various fields for convenient and efficient data transmission. Different from commonly used assumption of the time-invariant wireless channel, we focus on the research on the time-varying wireless downlink channel to get close to the practical situation. Our objective is to gain the maximum value of sum rate in the time-varying channel under the some constraints about cut-off signal-to-interference and noise ratio (SINR), transmitted power and beamforming. In order to adapt the rapid changing channel, we abandon the frequently used algorithm convex optimization and deep reinforcement learning algorithms are used in this paper. From the view of the ordinary measures such as power control, interference incoordination and beamforming, continuous changes of measures should be put into consideration while sparse reward problem due to the abortion of episodes as an important bottleneck should not be ignored. Therefore, with the analysis of relevant algorithms, we proposed two algorithms, Deep Deterministic Policy Gradient algorithm (DDPG) and hierarchical DDPG, in our work. As for these two algorithms, in order to solve the discrete output, DDPG is established by combining the Actor-Critic algorithm with Deep Q-learning (DQN), so that it can output the continuous actions without sacrificing the existed advantages brought by DQN and also can improve the performance. Also, to address the challenge of sparse reward, we take advantage of meta policy from the idea of hierarchical theory to divide one agent in DDPG into one meta-controller and one controller as hierarchical DDPG. Our simulation results demonstrate that the proposed DDPG and hierarchical DDPG performs well from the views of coverage, convergence and sum rate performance.
This paper investigates the end-to-end throughput maximization problem for a two-hop multiple-relay network, with relays powered by simultaneous wireless information and power transfer (SWIPT) technique. Nonlinearity of energy harvester at every relay node is taken into account and two models for approximating the nonlinearity are adopted: logistic model and linear cut-off model. Decode-and-forward (DF) is implemented, and time switching (TS) mode and power splitting (PS) mode are considered. Optimization problems are formulated for TS mode and PS mode under logistic model and linear cut-off model, respectively. End-to-end throughput is aimed to be maximized by optimizing the transmit power and bandwidth on every source-relay-destination link, and PS ratio and/or TS ratio on every relay node. Although the formulated optimization problems are all non-convex. Through a series of analysis and transformation, and with the aid of bi-level optimization and monotonic optimization, etc., we find the global optimal solution of every formulated optimization problem. In some case, a simple yet optimal solution of the formulated problem is also derived. Numerical results verify the effectiveness of our proposed methods.
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