Series Editorial: The Third Issue of the Series on Machine Learning in Communications and Networks

Geoffrey Y. Li, Walid Saad, Ayfer Ozgur, Peter Kairouz, Zhijin Qin, Jakob Hoydis, Zhu Han, Deniz Gunduz, Jaafar Elmirghani
2021 IEEE Journal on Selected Areas in Communications  
Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communication systems. In addition to those already published, we include in this issue 27 articles that have been submitted to the call. In the following, we provide a brief review of key contributions of papers in this issue according to their topics. II. INVITED PAPERS The invited paper, titled "Interplay between RIS and AI in Wireless Communications:
more » ... Fundamentals, Architectures, Applications, and Open Research Problems," by Wang et al., comprehensively overviews the state-of-the-art of artificial intelligence (AI)-enhanced reconfigurable intelligent surfaces (RISs), and explores the path to integrating AI with RISs in future wireless networks. III. SIGNAL PROCESSING This issue consists of seven papers that address various problems in signal processing using machine learning. The paper, titled "Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays," by Hu et al., uses deep reinforcement learning (DRL) for the beam selection and deep-unfolding neural network (NN) for the digital precoding optimization. The design outperforms the existing ones in terms of complexity and robustness. In the paper, titled
doi:10.1109/jsac.2021.3087366 fatcat:57l7wm4lljgt5n2ogqqlze6gvm