Data Analytics and Optimization for Hybrid Communication Systems

Dear Colleagues,

The last decade has experienced an upsurge of contributions aimed at validating the applicability of novel supervised learning models, clustering schemes and optimization algorithms to problems arising from a wide variety of fields. One such application scenario gravitates on the different paradigms that stem from the optimized management and operation of next-generation communication systems, especially in what regards the deployment of hybrid WAN/LAN/BAN wired/wireless networks, the incorporation of highly-directive transmission technologies, such as mmWave communications, the exploitation of massively captured information by such networks (coming either from its own operational processes or from the users of the network itself) and the consideration of energy efficiency in the allocation of radio resources. In all such paradigms, the use of novel optimization approaches (including the latest advances in swarm intelligence and bioinspired computation) and predictive modeling are called to play a crucial role to cope with the intrinsic challenges of an optimized, adaptive and efficient management and operation of such hybrid networks.

This rationale motivates the launch of a Special Issue of Algorithms is devoted to technical  contributions in the wide fields of data analytics (clustering, predictive modeling) and optimization (with a focus on bioinspired solvers), applied to the management and optimization of hybrid communication systems. Specific applications of interest include, but are not limited to:

• Hybrid wireless/wired networking
• mmWave/uWave communications
• Body Area Networks
• Multiple user communications, including interference avoidance/alignment
• Synchronization methods
• Radio resource management (e.g., spectrum, power)
• Channel estimation and equalization
• Source/channel coding/decoding
• Data-based routing protocols
• Energy-efficient network topologies
• Dynamic resource allocation
• Data fusion techniques
• Cross-layer synergies among physical and upper (e.g., network) layers
• Implementation issues
• Survivability and reliability
• Network deployment
• Quality of Service / Quality of Experience
• Congestion control

Prof. Dr. Javier Del Ser Lorente
Guest Editor

Manuscript Submission Information

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