Series Editorial The Fourth 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
2022 IEEE Journal on Selected Areas in Communications  
HE third call for papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communications, from which we have included 26 original contributions in this issue. In the following, we provide a brief review of key contributions of papers in this issue according to their topics. II. INVITED PAPERS In the invited paper [A1], Letaief et al. explore the scalable and trustworthy edge
more » ... ificial intelligence for 6G. It puts particular focus on the new design principles, service-driven resource allocation, end-to-end structure, implementations, and standardization of edge AI communication systems. III. SIGNAL PROCESSING This issue consists of six papers that address various problems in signal processing using machine learning. In [A2], Hussain and Michelusi provide an approach for beam training technique in mm-Wave systems with low overhead. Specifically, a dual-timescale design framework is adopted where the long-timescale corresponds to a frame duration while the short-timescale corresponds to a slot duration. It has been verified that the proposed design outperforms the benchmarks significantly. In [A3], Wu et al. propose a neural network to compensate for the non-linearity of the power amplifier and in-phase and quadrature imbalance. The proposed architecture uses a novel design providing a shortcut for the input. Weight punning is used to trade-off the computational complexity and accuracy. The authors also provide effective pruning methods for the proposed neural network structure. In [A4], Zhang et al. propose a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt
doi:10.1109/jsac.2021.3126188 fatcat:6aohhlq55fco5gnndq6cusjbbi