Special Issue "Radio Access Network Planning and Management"
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 8851
Interests: 5G mobile communication; RAN planning and management; RAN optimization; RAN fault management; wireless technologies and networks; radio resource management; self-organizing networks; network slicing; network softwarization and virtualization; artificial-intelligence-based radio resource management; big data and network data analytics; spectrum sharing
Interests: mobile communications networks; 5G networks; data analytics; fault management; radio access network optimization; machine learning application for network management; self-organizing networks; proactive network management
The advent of new broadband services and the Internet of Things era is leading to a revolutionary shift in the way mobile networks are managed. One of the biggest challenges is how to meet the stringent requirements for extremely high capacity density and extremely low latency in future radio access networks (RANs). 5G and beyond networks are envisioned to be much denser in terms of the number of access points and users, making network planning an arduous task with a continuous increase in CAPEX.
Moreover, mobile operators need to effectively deal with the complexity of operation of a multi-vendor, multi-technology, and softwarized RAN, while keeping OPEX and CAPEX as low as possible. As mobile operators migrate from the current physical networks to hybrid networks that support virtualized functions, capacity challenges grow significantly. Interoperability with legacy technologies, the use of optical wireless communication technologies such as Li-Fi, and new paradigms such as O-RAN, network slicing, and mobile edge computing will mitigate the cost of 5G and beyond network deployments.
Artificial intelligence (AI), machine learning (ML), and big data will also play an indisputable role in RAN planning and management. The paradigm of self-organizing networks (SONs) introduced in 4G networks needs to be evolved towards a set of disruptive technologies that leverage the unprecedented levels of computational capacity and take advantage of end-to-end intelligence. Network management is foreseen to support a flexible architecture that brings intelligence from centralized computing facilities to end terminals, going one step beyond the classification and prediction tasks that have been considered so far. The use of deep learning techniques for data analytics and metric cross-correlation will facilitate multi-objective, holistic optimization as well as near-real-time anomaly detection, enabling proactive resolution and root cause analysis, which are missing in current SON approaches.
This Special Issue aims at collecting contributions concerning planning, optimization, configuration, and fault management related to 5G and beyond RANs. Potential authors are invited to submit original research articles and review papers on the topics covered in this Special Issue, which include but are not limited to the following:
- ML and big data for RAN management and orchestration;
- Self-organizing networks evolution towards intelligent RANs;
- AI/ML-driven coverage, capacity, and frequency planning;
- Data-driven fault detection, diagnosis, and prediction;
- Deep and reinforcement learning for RAN planning;
- AI/ML for cloud-RAN planning and resource management;
- Management of softwarized and virtualized RAN;
- Capacity planning for mobile edge computing;
- Open-source RAN planning and optimization tools;
- Planning and management of non-public networks
- Indoor network planning and small cell deployments;
- Network access backhaul integration and planning;
- RAN topology design and optimization at different layers;
- Planning methods and tools for ultra-dense networks;
- RAN slicing optimization and intelligent brokering mechanisms;
- Advanced spectrum planning and management;
- RAN management automation using geo-localized data;
- Innovative architectures for intelligent RAN management;
- Multi-tenant, multi-vendor, multi-technology RAN management;
- Intent and policy-based management for intelligent RANs;
- End-to-end performance evaluation for RAN management;
- Economic aspects of RAN planning and operations.
Prof. Dr. Pablo Muñoz Luengo
Prof. Dr. Isabel de la Bandera Cascales
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Radio resource management
- Self-organizing networks
- RAN dimensioning and planning
- RAN optimization RAN fault management
- AI-driven radio network management
- Ultra-dense networks
- Small cells deployments
- RAN slicing RAN sharing
- Radio network data analytics
- Spectrum planning