This book brings together papers from the 2018 International Conference on Communications, Signal Processing, and Systems, which was held in Dalian, China on July 1416, 2018. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications, signal processing and systems. It is aimed at undergraduate and graduate electrical engineering, computer science and mathematics students, researchers and engineers from academia and industry as well as government employees.

Dr. Qilian Liang is a Professor at the Department of Electrical Engineering, University of Texas at Arlington (UTA), USA, where he has been working since August 2002. Prior to this, he was a member of the technical staff at Hughes Network Systems Inc. in San Diego, California. Dr. Liang received his B.S. degree from Wuhan University, China, in 1993; his M.S. degree from Beijing University of Posts and Telecommunications, China in 1996; and a Ph.D. degree from the University of Southern California (USC), Los Angeles, USA in May 2000, all in Electrical Engineering. His research interests include communications, signal processing and fuzzy logic systems. Dr. Liang has published more than 270 journal and conference papers, 6 book chapters, and has 6 U.S. patents pending. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award; 2003 U.S. Office of Naval Research (ONR) Young Investigator Award; 2005 UTA College of Engineering Outstanding Young Faculty Award; 2007,2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award; 2012 UTA College of Engineering Excellence in Research Award; 2013 UTA Outstanding Research Achievement or Creative Activity Award; and was inducted into UTA Academy of Distinguished Scholars in 2015.

Based on Deep Learning CSI Recovery for Uplink Massive Device Dynamic Internet of Thing(DIoT).- DEEP: Detection of Environmental Pollution Using Cooperative Neural Network.- Imbalanced data classification method based on Ensemble learning.- Bayesian methods based Learning Automata for two-player stochastic games with incomplete information.- A double competitive strategy based learning automata algorithm.