Cloud and Fog Radio Access Networks: Information, Communications, Inference and Learning Theoretic Views
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 2362
Special Issue Editors
Interests: multi-user information theory; modern communication networks (cloud and fog radio networks); information and signal processing (information–estimation); information bottleneck problems in communications and learning; sparse communications models and non-orthogonal (NOMA) systems
Special Issues, Collections and Topics in MDPI journals
Interests: network information theory; statistical decision theory; data compression; security and privacy
Interests: communication theory; information theory; wireless communication systems; joint source-channel coding; cooperative communications
Special Issue Information
Cloud radio access networks (C-RAN) emerge as appealing architectures for next-generation wireless/cellular systems whereby the processing/decoding is migrated from the local base-stations/radio units (RUs) to one, or multiple, control/central units (CU) in the “cloud”. This is the basic network feature of future cell-less wireless technology. Fog radio access networks (F-RAN) address the case where the RUs are enhanced by having the ability of local caching of popular contents. This special issue is focused on the information-theoretic and communications aspects of such networks.
Moreover, such frameworks of C-RAN, with oblivious signal processing at the RUs, have intimate connections on the theoretical and practical levels, to what is known as Distributed Information Bottleneck (IB) setting, and as such with basic network information theoretic frameworks such as: Remote Source-Coding, Information Combining, Chief-Executive-Officer (CEO) or Source Coding under Log-Loss, just to name a few. Evidently, learning, and distributed learning procedures will play a central role in future communications systems, and, as such, are important components also in C-RAN and F-RAN, let alone the fact that a variety of IB procedures are, stand alone, important tools in the mathematical analysis of learning methodologies (with focus on Deep Learning).
This special issue of the Entropy journal is planned to focus on Information and Communications Theoretic aspects and views that are associated with the general framework of C-RAN, F-RAN, and IB. The outlook accounts also for multiple connections, both on the theoretical and practical levels, that emerge with distributed learning and estimation in this C-RAN, F-RAN framework, as well as, learning approaches for resource allocation and network optimization.
Prof. Dr. Shlomo Shamai (Shitz)
Prof. Dr. Abdellatif Zaidi
Dr. Iñaki Estella Aguerri
Dr. Alon Kipnis
Guest Editors
Manuscript Submission Information
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Keywords
- C-RAN
- F-RAN
- Information Bottleneck
- Cell-less wireless networks
- Federated learning
- Distributed learning
- Learning for resource allocation and network optimization
