We study the impact of nonlinear saturation effect and receiver architecture on the performance of multiple-input and multiple-output (MIMO) wireless power transfer (WPT). We propose a generic hybrid combining receiver architecture where the receive antennas are grouped into multiple subarrays, each subarray consisting of multiple energy harvesting (EH) circuits. For efficient receiver reconfiguration, a power splitter is placed in between each antenna subarray and the associated EH circuits. To improve the WPT performance, we formulate an optimization problem for joint transmit and receive beamforming design along with receiver reconfiguration, which appears non-convex fractional programming. To resolve this, the optimization problem is reformulated into equivalent semi-definite programming problem with rank-one relaxation through quadratic transform, which provides a suboptimal solution for the original problem. Then, we propose an iterative algorithm for optimizing the joint beamforming design and receiver reconfiguration. We confirm that the WPT system optimization considering the nonlinearities existing in the proposed generic receiver structure yields better performance than when the nonlinearities and resulting receiver architecture are not taken into account.

In this paper, we propose a unified simultaneous wireless information and power transfer (SWIPT) signal and its architecture design in order to take advantage of both single tone and multi-tone signaling by adjusting only the power allocation ratio of a unified signal. For this, we design a novel unified and integrated receiver architecture for the proposed unified SWIPT signaling, which consumes low power with an envelope detection. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition for low-power Internet-of-Things (IoT) devices. This will facilitate effective deployment of low-power IoT networks that concurrently supply both information and energy wirelessly to the devices by using the proposed unified SWIPT signaling and architecture.

In this article, we propose a unified simultaneous wireless information and power transfer (SWIPT) signaling and architecture to take advantage of both single tone and multitone signaling by adjusting only the power allocation ratio of a unified signal. Toward this, we design a unified and integrated receiver architecture for the proposed unified SWIPT signaling via an envelope detection with low power consumption. To lower the computational complexity of the receiver, we propose an adaptive control algorithm where the transmitter adjusts the communication mode through temporal convolutional network (TCN)-basedasymmetric processing. To this end, the transmitter optimizes the modulation index and power allocation ratio in short-term scale while updating the mode switching threshold in long-term scale. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition at low-power Internet of Things (IoT) devices. Consequently we will facilitate the effective deployment of low-power IoT networks that concurrently supply both information and energywirelesslyto the devices by using the proposed unified SWIPT and adaptive control algorithm at the transmitter side.

We propose a heterogeneously reconfigurable energy harvester which contains multiple energy harvesting (EH) blocks, each comprised of multiple EH circuits in parallel to be optimized for different input power ranges. Practical EH circuits have their own favorable input power range due to turn-on sensitivity and saturation effects. To account for this, we present the necessary conditions for efficient configuration of an energy harvester, and then propose an algorithm for optimal reconfiguration of the energy harvester. Furthermore, adaptive time switching and mode switching problems are formulated in order to maximize the average achievable rate under the self-powering condition. Our proposed energy harvester and algorithm can be applied to low-power Internet-of-Things (IoT) devices with wireless EH capability for self-sustainability, which will be a key enabler for realizing a battery-free IoT network.

In this article, we propose a dual-mode simultaneous wireless information and power transfer (SWIPT) system with a deep-learning-based adaptive mode switching (MS) algorithm to exploit both advantages of single-tone and multitone SWIPT. For self-powering of low-energy Internet-of-Things (IoT) devices, a duty-cycling operation is used with nonlinear energy harvesting. For this, we employ a new energy-assisted single-tone modulation which simplifies the receiver structure for information decoding. Considering the symbol-error rate performance, we formulate an adaptive MS problem to maximize the achievable rate under the energy-causality constraint by adjusting the MS threshold. To relieve the computational burden of the receiver, we introduce asymmetric processing for adaptive MS, for which the transmitter adapts the communication mode based on the feedback from the receiver. We invoke deep learning for adaptive MS at the transmitter that iteratively updates the MS threshold in a long-term scale via deep long short-term memory (LSTM) recurrent neural network (RNN) while deciding on the communication mode and modulation index in a short-term scale. We demonstrate the achievable rate improvement under an energy-neutral operation while providing interesting insights into designing the adaptive MS algorithm for the dual-mode SWIPT system.

This paper proposes a new reconfigurable energy harvester for simultaneous wireless information and power transfer (SWIPT), where the energy harvester has multiple energy harvesting (EH) circuits in parallel and is adaptively reconfigured depending on the received power. Unlike the existing ideal linear EH model, where EH efficiency is a fixed constant regardless of the received power, we consider the practical nonlinear EH model. With the new reconfigurable energy harvester, we study adaptive mode switching between information decoding and EH over the fading channel. We optimize the problem of maximizing the average achievable rate under the energy causality condition, namely an average harvested energy constraint. Then we evaluate the rate-energy tradeoff for the proposed reconfigurable energy harvester. Numerical results demonstrate considerable performance gains through the proposed energy harvester. This implies that the SWIPT receiver can be self-powered effectively with the proposed one while meeting the energy causality safely.

In this paper, we propose a dual mode simultaneous wireless information and power transfer (SWIPT) system in which a sensor node monitors the received power and adaptively controls the communication mode. To this end, we design two types of single and multi-tone waveforms and propose a new transceiver architecture that realizes adaptive power management and information decoding (adaptive PM-&-ID) module. On top of this, we implement adaptive PM-&-ID policy algorithm which reflects non-linear energy harvesting (EH) model for both single and multi-tone waveforms. Our newly designed SWIPT can be applied to Internet-of-Things (IoT) network with RF EH capability for self-powering, leading to battery-free network. Numerical results show that significant performance gain can be achieved by the proposed dual mode SWIPT over the existing SWIPT design.