Abstract
This paper focuses on Beyond fifth generation (B5G) non-linear data modeling and decision-making tools to optimize cost reduction versus coverage-QoS trade-off, in other words, the number of active Remote Radio Heads or Units (RRHs) needed according to traffic demands. The cost and energy optimization are analytically expressed by modeling the complex relationships between input and output system parameters using realistic scenarios and traffic profiles for low, medium, and high traffic environments. The optimization tool is based on a multi-objective integer linear programming model, designed to reduce the network cost while maintaining a good coverage-QoS and accounting for capacity constraints, User Equipments (UEs), and different slices. Results at 3.6 and 28 GHz are presented by analyzing and comparing several Cloud Radio Access Network (C-RAN) split options in a heterogeneous deployment with Macro-RRHs (MRRHs) and Small-RRHs (SRRHs). Cost reductions ranging from 30 % to 70 % have been obtained depending on the scenario. This proposal allows mobile network operators (MNOs) to achieve further optimization, while providing better network diagnostics.
Highlights
The Fifth-Generation (5G) of cellular networks has a service-oriented architecture that significantly increases both performance and flexibility of the offered services to users and service providers
These results demonstrate how the algorithm reduces the number of required active Remote Radio Heads or Units (RRHs)
It offers acceptable coverage and satisfies the User Equipments (UEs) requirements in terms of Quality of Service (QoS). This is crucial because it entails a considerable reduction in the network cost, with the consequent improvements in energy-saving, necessary when a large amount of cells are deployed as it is the case of 5G and beyond
Summary
The Fifth-Generation (5G) of cellular networks has a service-oriented architecture that significantly increases both performance and flexibility of the offered services to users and service providers. This is done by introducing new radio modes such as ultra-Reliable Low Latency Communication (uRLLC), Massive Machine Type Communications (mMTC) and enhanced Mobile Broadband (eMBB). The future Sixth-Generation (6G) network ecosystem is a step further that should implement a fully cloud-native architecture capable of dealing with a network of subnetworks, offering Tbps of data throughput, sub-ms latency and extremely low packet error rate, increased device. Future mobile networks should support immersive communication, cognition and twinning, deterministic end-to-end applications, and high-resolution sensing services. The enormous increase in the number of devices, data amounts, and data rates implies an increase in the overall data traffic and required capacity, while the energy reduction is not automatically guaranteed
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