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Titlebook: Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication; Proceedings of MDCWC E. S. Gopi Conference proce

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书目名称Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication
副标题Proceedings of MDCWC
编辑E. S. Gopi
视频video
概述Presents research works in various fields of computational intelligence and machine learning.Discusses results of MDCWC 2020 held at National Institute of Technology Tiruchirappalli, India.Serves as a
丛书名称Lecture Notes in Electrical Engineering
图书封面Titlebook: Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication; Proceedings of MDCWC E. S. Gopi Conference proce
描述This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include  the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, powercontrol, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalizati
出版日期Conference proceedings 2021
关键词Mobile Data Analysis; Mobility Analysis; Network Control and Security; Particle Swarm Optimization; Gene
版次1
doihttps://doi.org/10.1007/978-981-16-0289-4
isbn_softcover978-981-16-0291-7
isbn_ebook978-981-16-0289-4Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightSpringer Nature Singapore Pte Ltd. 2021
The information of publication is updating

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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication978-981-16-0289-4Series ISSN 1876-1100 Series E-ISSN 1876-1119
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LSTM Network for Hotspot Prediction in Traffic Density of Cellular Network depends on numerous factors like time, location, number of mobile users connected and so on. It exhibits spatial and temporal relationships. However, only certain regions have higher data rates, known as hotspots. A hotspot is defined as a circular region with a particular centre and radius where t
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Generative Adversarial Network and Reinforcement Learning to Estimate Channel Coefficientsincreasing need to guarantee accuracy. There is little value in large data rates if the channel state information (CSI) is subject to frequent contamination. In the context of massive MIMO systems, error in decoding the signal is introduced mainly due to two key factors: (i) intercell interference (
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Novel Method of Self-interference Cancelation in Full-Duplex Radios for 5G Wireless Technology Usingnique, same set of frequency channels is used for simultaneous uplink and downlink signal transmissions and hence is termed as full-duplex (FD) communications or full-duplex radios. However, a major shortcoming of this technique is the presence of self-interference (SI), which arises due to the pres
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