Machine and Deep Learning Techniques in Wireless and Satellite Communication Networks

Dear Colleagues,

The standardization of 5G wireless networks (including a non-terrestrial component) is currently underway. Moreover, researchers have also focused on what 6G will become, making progress on designing the new architecture. The applications of artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques in wireless and satellite communications networks have drawn significant attention recently and are applied in the design of 5G wireless networks as well as used as built in functionalities in 6G networks. ML and DL techniques enable wireless communications in order to fulfill increasing and diverse requirements across a large range of application scenarios. Additionally, there are optimization algorithms for the solution of complex wireless networks. On the one hand, training a complex deep learning model is time consuming and can take hours, days, or even weeks. Their cooperation with an optimization algorithm directly affects the model’s training efficiency and finally its performance. On the other hand, understanding the principles of different optimization algorithms and the role of their hyperparameters will enable us to tune the hyperparameters in a targeted manner to improve the performance of deep learning models. In the proposed Special Issue, we expect papers on the above subjects and on classical wireless scenarios, such as channel modeling; physical layer up intelligent resource allocation algorithms and the consideration of the ML and DL techniques as services.

Topics of interest include but are not limited to the following:

• ML, DL techniques for Physical Layer Design including signal detection, classification and compression;
• ML, DL techniques for spectrum sensing and positioning;
• ML, DL techniques for channel modeling, radio propagation, estimation and prediction;
• ML, DL techniques for resource allocation and wireless and satellite networks optimization;
• ML, DL techniques for new emerging applications toward 6G (including intelligent reflection surface, unmanned aerial vehicles, the Internet of Things, etc.)
• ML, DL techniques for vehicular networks;
• Distributed machine learning/federated learning and communications;
• AI, ML and DL for Satellite Communications including GEO, MEO and LEO Constellations;
• AI, ML and DL for Satellite Communications including UAVs and Micro Satellite Systems;
• AI, ML and DL for Cognitive Radio Communications and Networks.

Prof. Dr. Athanasios D. Panagopoulos
Guest Editor

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