Network slicing has been a significant technological advance in the 5G mobile network allowing delivery of diverse and demanding requirements. The slicing grants the ability to create customized virtual networks from the underlying physical network, while each virtual network can serve a different purpose. One of the main challenges yet is the allocation of resources to different slices, both to best serve different services and to use the resources in the most optimal way. In this paper, we study the radio resource slicing problem for Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) as two prominent use cases. The URLLC and eMBB traffic is multiplexed over multiple numerologies in 5G New Radio, depending on their distinct service requirements. Therein, we present our optimization algorithm, Mixed-numerology Mini-slot based Resource Allocation (MiMRA), to minimize the impact on eMBB data rate due to puncturing by different URLLC traffic classes. Our strategy controls such impact by introducing a puncturing rate threshold. Further, we propose a scheduling mechanism that maximizes the sum rate of all eMBB users while maintaining the minimum data rate requirement of each eMBB user. The results obtained by simulation confirm the applicability of our proposed resource allocation algorithm.
Ultra-reliable low latency communication (URLLC), which refers to achieving almost 100% reliability at a certain (satisfactory) level of services and stringent latency, is one of the key requirements for 5G networks. However, most prior studies on reliable communication did not address space domain analysis. Neither were they pursued from a dependability perspective. This paper addresses the ultra-reliable communication (URC) aspect of URLLC and aims at advocating the concept of URC from a dependability perspective in the space domain. We perform in-depth analysis on URC considering both the spatial characteristics of cell deployment and user distributions, as well as service requirements. We first introduce the concepts of cell availability and system availability in the space domain, then perform connectivity-based availability analysis by considering a Voronoi tessellation where base stations (BSs) are deployed according to a certain distribution. Moreover, we investigate the relationship between signal-to-interference-plus-noise ratio (SINR), user requirement, and achievable cell or system availability by employing both Poisson point process (PPP) and determinantal point process (DPP) BS distributions. For SINR-based availability analysis, coverage contours are identified. Considering further the user distribution in a region of interest, expressions for system availability are derived from users' perspective. Furthermore, we propose an algorithm which could be used for availability improvement based on the calculated availability level. Numerical results obtained considering diverse network scenarios and cell deployments with multiple cells and multiple topologies illustrate the achievable availability under various circumstances.
5G technology complements the enabling of communication services for different vertical industries such as smart distribution grids. Automation is an integral and necessary part of the power distribution grid operation and management. This paper postulates a framework by which the smart distribution grid can obtain service-oriented communication services using 5G network slicing and intent-based networking (IBN). IBN provides an interface to service users and network stakeholders to cooperate through a high level abstraction model of service provisioning in a network agnostic manner. The automation and adaptability of the distribution grid are facilitated by using the dynamic and closed-loop mechanism of IBN together with network slicing and network function virtualization for network management and orchestration. We identify the automation parts of the power distribution grid and illustrate the intent processing and its inclusion in the definition of network slice instances, service and network configuration models.
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