Modulation and Coding Design for Simultaneous Wireless Information and Power Transfer

In order to satisfy the power demands of IoT devices and thus extend their lifespan, radio frequency (RF) signal aided wireless power transfer (WPT) is exploited for remote charging. Carefully coordinating both the WPT and wireless information transfer (WIT) yields an emerging research trend in simultaneous wireless information and power transfer (SWIPT). However, SWIPT systems designed by assuming Gaussian distributed input signals may suffer from a substantial performance degradation in practice, when the finite alphabetical input is considered. In this article, we will provide a design guide of coding controlled SWIPT and study the modulation design in both single-user and multi-user SWIPT systems. We hope this guide may push SWIPT a step closer from theory to practice

Joint Interleaver and Modulation Design For Multi-User SWIPT-NOMA

Radio frequency (RF) signals can be relied upon for conventional wireless information transfer (WIT) and for challenging wireless power transfer (WPT), which triggers the significant research interest in the topic of simultaneous wireless information and power transfer (SWIPT). By further exploiting the advanced non-orthogonal-multiple-access (NOMA) technique, we are capable of improving the spectrum efficiency of the resource-limited SWIPT system. In our SWIPT system, a hybrid access point (H-AP) superimposes the modulated symbols destined to multiple WIT users by exploiting the power-domain NOMA, while WPT users are capable of harvesting the energy carried by the superposition symbols. In order to maximize the amount of energy transferred to the WPT users, we propose a joint design of the energy interleaver and the constellation rotation-based modulator in the symbol-block level by constructively superimposing the symbols destined to the WIT users in the power domain. Furthermore, a transmit power allocation scheme is proposed to guarantee the symbol-error-ratio (SER) of all the WIT users. By considering the sensitivity of practical energy harvesters, simulation results demonstrate that our scheme is capable of substantially increasing the WPT performance without any remarkable degradation of the WIT performance

Fully Automated Bone Age Assessment On Large-Scale Hand X-Ray Dataset

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance

A Fair Resource Allocation Algorithm for Data and Energy Integrated Communication Networks

With the rapid advancement of wireless network technologies and the rapid increase in the number of mobile devices, mobile users (MUs) have an increasing high demand to access the Internet with guaranteed quality-of-service (QoS). Data and energy integrated communication networks (DEINs) are emerging as a new type of wireless networks that have the potential to simultaneously transfer wireless energy and information via the same base station (BS). This means that a physical BS is virtualized into two parts: one is transferring energy and the other is transferring information. The former is called virtual energy base station (eBS) and the latter is named as data base station (dBS). One important issue in such setting is dynamic resource allocation. Here the resource concerned includes both power and time. In this paper, we propose a fair data-and-energy resource allocation algorithm for DEINs by jointly designing the downlink energy beamforming and a power-and-time allocation scheme, with the consideration of finite capacity batteries at MUs and power sensitivity of radio frequency (RF) to direct current (DC) conversion circuits. Simulation results demonstrate that our proposed algorithm outperforms the existing algorithms in terms of fairness, beamforming design, sensitivity, and average throughput.</jats:p