Artificial Intelligence for Physical-Layer Wireless Communications

Submission Deadline: 31 December 2019 IEEE Access invites manuscript submissions in the area of Artificial Intelligence for Physical-Layer Wireless Communications. Artificial Intelligence (AI), including Deep Learning (DL) and deep reinforcement learning (DRL) approaches, well known from computer science (CS) disciplines, are beginning to emerge in wireless communications. These AI 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 Section in IEEE Access 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:
  • DL/DRL based 5G wireless technologies
  • DL/DRL based beamforming in mmWave massive MIMO
  • DL/DRL based hybrid precoding in massive MIMO system, mmWave system
  • DL/DRL based non-orthogonal multiple access (NOMA) techniques
  • DL/DRL based MIMO-NOMA frameworks
  • DL/DRL based sparse channel estimation
  • DL/DRL based communication frameworks
  • DL/DRL based multiuser detection
  • DL/DRL based modulation and coding
  • DL/DRL based direction-of-arrival estimation
  • DL/DRL based channel modeling
  • DL/DRL based signal classification
  • DL/DRL based unmanned aerial vehicles (UAVs) techniques
  • DL/DRL based energy-efficient network operations
  • DL/DRL based ultra-dense cell communication
  • DL/DRL based testbeds and experimental evaluations
  We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.   Associate Editor:  Guan Gui, Nanjing University of Posts and Telecommunications, China Guest Editors:
  1. Tomohiko Taniguchi, Fujitsu Laboratories Limited, Japan
  2. Haris Gacanin, Nokia Bell Labs, Belgium
  3. Ning Zhang, Texas A&M University at Corpus Christi, USA
  4. Yue Cao, Northumbria University, UK
  5. Kezhi Wang, Northumbria University, UK
  Relevant IEEE Access Special Sections:
  1. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  2. Applications of Big Data in Social Sciences
  3. Artificial Intelligence and Cognitive Computing for Communications and Networks
IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland Paper submission: Contact Associate Editor and submit manuscript to: For inquiries regarding this Special Section, please contact: