Intelligent Resource Allocation for 5G beyond Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 204

Special Issue Editor


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Guest Editor
Interdisciplinary Laboratory of Digital Sciences, Department of Computer Science, University of Paris-Saclay, 91190 Gif-sur-Yvette, France
Interests: quality of service; tactile internet; SDN; machine learning

Special Issue Information

Dear Colleagues,

The fifth generation (5G) and beyond of mobile network technology has attracted a lot of research interest recently. The 5G technology promises to provide data delivery a hundred times (10 gigabit/seconds) faster than the current 4G networks. Therefore, it is expected to simultaneously serve large number of devices with multiple heterogeneous services and diverse QoS requirements, such as throughput, delay, reliability, etc. Fifth-generation wireless systems are designed to support three generic services with heterogeneous requirements: extreme mobile broadband (eMBB), massive-machine-type communication (mMTC) and ultra-reliable and low latency communications (URLLC).

These services require a high data rate, secure connections with low latency and reliable broadband access over large areas. They support a large number of Internet of Things (IoT) devices that are only active intermittently and send small payloads of data. They further require wireless connectivity for the massive deployment of devices typically requiring low transmission rates in the uplink. This is why methods of resource allocations is required in such kind of networks that require different Quality of Service.

This Special Issue focuses on state-of-the-art resource allocation in B5G networks where intelligence plays a significant role in services and application. We invite high-quality paper submissions of a theoretical or experimental nature on topics including, but not limited to, the following:

  • AI-based resource allocation in B5G;
  • Optimization methods of resource allocation;
  • Admission control and scheduling mechanisms;
  • SDN for network automatization;
  • End-to-end QoS/QoE for intelligent resource management;
  • Scalability and complex solutions for intelligent resource allocation.

Dr. Tara Ali-Yahiya
Guest Editor

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Keywords

  • quality of service
  • machine learning
  • B5G
  • resource allocation

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Published Papers

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