Decode-and-Forward Relay Based Bidirectional Wireless Information and Power Transfer

In this paper, we propose the decode-and-forward(DF) based bidirectional wireless information and power transfer(BWIPT) in two-hop relay systems, where the bidirectional relay can decode and forward information from the user to the access point(AP), and assist the wireless power transfer from the AP to the user. To maximize the information rate from the user to the AP, we derive the closed form expression of the optimal power splitting(PS) factor, and the time allocation scheme to obtain the optimal time switching(TS) factor. Simulation results show that for both PS and TS protocols, the proposed DF based bidirectional relay systems can improve the information rate as compared with the amplify-and-forward(AF) based bidirectional relay systems.

Age of Transmission-Optimal Scheduling for State Update of Multi-Antenna Cellular Internet of Things

Timely information updates are critical for real-time monitoring and control applications in the Internet of Things(IoT). In this paper, we consider a multi-antenna cellular IoT for state update where a base station(BS) collects information from randomly distributed IoT nodes through time-varying channel.Specifically, multiple IoT nodes are allowed to transmit their state update simultaneously in a spatial multiplex manner. Inspired by age of information(AoI),we introduce a novel concept of age of transmission(AoT) for the sceneries in which BS cannot obtain the generation time of the packets waiting to be transmitted. The deadline-constrained AoT-optimal scheduling problem is formulated as a restless multi-armed bandit(RMAB) problem. Firstly, we prove the indexability of the scheduling problem and derive the closed-form of the Whittle index. Then, the interference graph and complementary graph are constructed to illustrate the interference between two nodes. The complete subgraphs are detected in the complementary graph to avoid inter-node interference. Next, an AoT-optimal scheduling strategy based on the Whittle index and complete subgraph detection is proposed.Finally, numerous simulations are conducted to verify the performance of the proposed strategy.

Precoder Design and Impulsive Noise Mitigation Scheme for Industrial Wireless Communications

In industrial wireless scenarios,the impulsive noise(IN)incurred by machine running or operation causes a serious influence on the powerlimited industrial wireless communications.It is challenging to ensure efficient and reliable transmission with quality of service(QoS)guarantee for machinetype communication devices(MTCDs).Considering the IN in the industrial process,this paper establishes the multiuser multiple-input single-output(MU-MISO)orthogonal frequency division multiplexing(OFDM)system model,which combines transmitter and receiver design.Two precoding schemes are designed to improve communication effectiveness at the transmitter.More specifically,the precoder design scheme which combines semi-definite relaxation(SDR)with difference-of-two-convex-function(D.C.)iterative algorithm,is developed by utilizing the Dinkelbach method to improve the system effectiveness.To decrease the computational complexity,we devise the quadratic-based fractional programming(QFP)algorithm,which decouples the variables by using a quadratic transform method.On this basis,the IN mitigation scheme is studied to reduce the system error rate(SER)at the receiver.With the goal of improving the reliability of industrial wireless communications,we propose a hybrid nonlinear IN mitigation(HNINM)scheme and then derive its closed-form expression of SER.The simulation results show that the proposed QFP algorithm achieves superior performance while the HNINM scheme decreases the SER of industrial wireless communications.

Pregnant women’s awareness and acceptance of epidural anesthesia and its influence on cesarean section rate control in China: A qualitative study

Background: Epidural anesthesia, as an effective pain relief method, could be viewed as an attempt to encourage vaginal delivery and control cesarean section. Increased demand caused by psychosocial factors such as fear of childbirth and labor pain is supposed to be one predictable drive of high cesarean section rate in present China. Little qualitative information on women’s awareness and perceptions of epidural anesthesia was found, but conscious efforts should be focused on this part to help generate policy-making priority. Methods: This study was carried out under an exploratory descriptive design in Bao’an Maternity and Child Health Hospital. Those interested in participating would be invited to focus group discussion or individual interview according to a semi-structured open-ended guide after collecting the participants’ demographic characteristics. The thematic content analysis approach was used for data analysis. Results: Five major themes were identified: 1. the sources to gain information on epidural anesthesia;2. clinical impact;3. social impact on awareness;4. association between epidural anesthesia and cesarean section;5. attitudes and questions about the current service implementation. Discussions: The interplay between pregnancy knowledge, socio-economic conditions, social support, insurance policy environment, women’s judgment of health care quality influences the ways women acknowledge and utilize epidural anesthesia service. Conclusions: As maternal requested CS due to low pain tolerance emerges gradually, natural labor with epidural anesthesia is a more suitable childbirth option, which is also expected to function in reducing CS rate by service-providers and administrators in the health departments of China besides building up a pain-free labor environment.

A hybrid data-mining framework for train rescheduling strategy pattern discovery

This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies(RSs).The proposed model is composed of two states.In the first state,decision tree,random forest,gradient boosting decision tree(GBDT)and extreme gradient boosting(XGBoost)models are used to investigate the importance of features.The features that have a high influence on RSs are first selected.In the second state,a K-means clustering method is used to uncover the interdependences between RSs and the influencing features,based on the results in the first state.The proposed method can determine the quantitative relationships between RSs and influencing factors.The results clearly show the influences of the factors on RSs,the possibilities of different train operation RSs under different situations,as well as some key time periods and key trains that the controllers should pay more attention to.The research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs.