Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of ...
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ISBN:
(数字)9781728154671
ISBN:
(纸本)9781728154688
Mode selection is normally used in conjunction with Device-to-Device (D2D) millimeter wave (mmWave) communications in 5G networks to overcome the low coverage area, poor reliability and vulnerable to path blocking of mmWave transmissions. Thus, producing a high-efficient D2D mmWave using mode selection based on select the optimal mode with low complexity turns to be a big challenge towards ubiquitous D2D mmWave communications. In this paper, low complexity and high-efficient mode selection in D2D mmWave communications based on deep learning is introduced utilizing the artificial intelligence. In which, deep learning is used to estimate the optimal mode y in the case of blocking of mmWave transmission or low coverage area of mmWave communications. Then, the proposed deep learning model is based on training the model with almost use cases in offline phase to predict the optimal mode for data relaying high-reliability communication in online phase. In mode selection process, the potential D2D transmitter select the mode to transmit the data either based on dedicated D2D communication or through the cellular uplink using the base station (BS) as a relay based on several criteria. The proposed deep learning model is developed to overcome the challenges of selected the optimal mode with low complexity and high efficiency. The simulation analysis show that the proposed mode selection algorithms outperform the conventional techniques in D2D mmWave communication in the spectral efficiency, energy efficiency and coverage probability.
Internet of Things （IoT） is a platform and a phenomenon that allows everything to process information, commu-nicate data, analyze context collaboratively and in the service or individuals, organizations and business...
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Internet of Things （IoT） is a platform and a phenomenon that allows everything to process information, commu-nicate data, analyze context collaboratively and in the service or individuals, organizations and businesses. In theprocess of doing so, a large amount of data with different formats and content has to be processed efficiently, quick-ly and intelligently through advanced algorithms, techniques, models and tools. This new paradigm is enabled bythe maturity of several different technologies, including the internet, wireless communication, cloud computing,sensors, bigdata analytics and machine learning algorithms.
Objective: Systematically review the value of second-trimester triple serum tests screening for Down's syndrome by using meta analysis. Method: The literature on PubM ed, Journal of Medicine Library, CNKI, Web of ...
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Objective: Systematically review the value of second-trimester triple serum tests screening for Down's syndrome by using meta analysis. Method: The literature on PubM ed, Journal of Medicine Library, CNKI, Web of Knowledge from January 2000 to April 2014 concerning second-trimester Down's syndrome screening was retrieved by computer or by hand. The literature was evaluated based on the QUADAS quality evaluation standard. The basic fourfold table data were extracted from the literature. Statistical analysis was conducted by using SPSS and Meta-Disc1.4. Results: Altogether 166 related articles were retrieved. According to the standard of literature selection and QUADAS quality evaluation, eventually 14 articles were brought into research. The meta analysis results of second-trimester triple serum tests screening for Down's syndrome were as follows: the sensitivity was 77%, the specificity was 93%, + LR was 13.92,-LR was 0.26, DOR was 40.82, the AUC of SROC was 0.9133, and Q*= 0.8458. In addition, the results of subgroup analyses based on age showed that the pooled sensitivity of high age group is higher than low age group, which indicated that the second-trimester serum triple serum tests screening for Down's syndrome could achieve better results for advanced maternal age. Conclusion: The second-trimester triple serum tests screening for Down's syndrome had a certain sensitivity and specificity, but for the regions with good medical and health care, they could consider increasing the serum screening indexes or adding ultrasound screening method so as to further improve the detection rate of Down's syndrome children and reduce the false-positive rate.
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