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Deep Learning Methods for Physical-Layer Wireless Communications - Recent Advances and Future Trends

Deep Learning Methods for Physical-Layer Wireless Communications - Recent Advances and Future Trends

Journal
Impact Score 2.31

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Special Issue Information

Submission Deadline: 29-02-2020
Journal Impact Score: 2.31
Journal Name: Physical Communication
Publisher: Physical Communication

Special Issue Call for Papers


Overview



Deep Learning (DL) and deep reinforcement learning (DRL) methods, well known from computer science (CS) disciplines, are beginning to emerge in wireless communications. These approaches were first widely applied to the upper layers of wireless communication systems for various purposes, such as routing establishment/optimization, and deployment of cognitive radio and communication network. These system models and algorithms designed with DL technology greatly improve the performance of communication systems based on traditional methods.



New features of future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements, make traditional methods no longer suitable, and provides many more potential applications of DL. DL technology has become a new hotspot in the research of physical-layer wireless communications and challenges conventional communication theories. Currently, DL-based ‘black-box’ methods show promising performance improvements but have certain limitations, such as the lack of solid analytical tools and the use of architectures specifically designed for communication and implementation research. With the development of DL technology, in addition to the traditional neural network-based data-driven model, the model-driven deep network model and the DRL model (i.e. DQN) which combined DL with reinforcement learning, are more suitable for dealing with future complex communication systems. As in most cases of wireless resource allocation, there are no definite samples to train the model, hence DRL, which trains the model by maximizing the reward associated with different actions, can be adopted.



This Special Issue focuses on the application of DL/DRL methods to physical-layer wireless communications to make future communications more intelligent. We invite submissions of high-quality original technical and survey articles, which have not been published previously, on DL/DRL techniques and their applications for wireless communication and signal processing.



 



The topics of interests include, but are not limited to:



· Deep Learning based 5G wireless technologies



· Deep Learning based beamforming in mmWave massive MIMO



· Deep Learning based hybrid precoding in massive MIMO system, mmWave system



· Deep Learning based non-orthogonal multiple access (NOMA) techniques



· Deep Learning based MIMO-NOMA frameworks



· Deep Learning based sparse channel estimation



· Deep Learning based communication frameworks



· Deep Learning based multiuser detection



· Deep Learning based modulation and coding



· Deep Learning based direction-of-arrival estimation



· Deep Learning based channel modeling



· Deep Learning based signal classification



· Deep Learning based unmanned aerial vehicles (UAVs) techniques



· Deep Learning based energy-efficient network operations



· Deep Learning based ultra-dense cell communication



· Deep Learning based testbeds and experimental evaluations



 



Important Dates



· Initial paper submission deadline: February 29, 2020



· First round authors notification: May 29, 2020



· Invited revisions deadline: July 29, 2020



· Second round authors notification: September 15th, 2020



· Final revision deadline: November 30th, 2020



· Projected publication: Autumn 2020



 



Guest Editors



Dr. Xiaohui Yuan



Department of Computer Science and Engineering, University of North Texas, USA.



Director, Computer Vision and Intelligent Systems Lab, University of North Texas, USA



Email: [email protected]



Website: http://facultyinfo.unt.edu/faculty-profile?profile=xy0009



Dr. Andino Maseleno (Lead Guest Editor)



Institute of Informatics and Computing Energy



Universiti Tenaga Nasional, Malaysia.



Email: [email protected]



Google Scholar: https://scholar.google.com/citations?user=HfQ2jGIAAAAJ&hl=en



Dr. Mohammad Hammoudeh



Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.



Email: [email protected]



Google Scholar: https://scholar.google.com/citations?user=gy9CfrUAAAAJ&hl=en

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