5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View

By Petar Popovski, Kasper Fløe Trillingsgaard, Osvaldo Simeone, Giuseppe Durisi. 2018

844 citations recorded by the source corpus.

The grand objective of 5G wireless technology is to support three generic services with vastly heterogeneous requirements: enhanced mobile broadband (eMBB), massive machine-type communications (mMTCs), and ultra-reliable low-latency communications (URLLCs). Service heterogeneity can be accommodated by network slicing, through which each service is allocated resources to provide performance guarantees and isolation from the other services. Slicing of the radio access network (RAN) is typically done by means of orthogonal resource allocation among the services. This paper studies the potential advantages of allowing for non-orthogonal sharing of RAN resources in uplink communications from a set of eMBB, mMTC, and URLLC devices to a common base station. The approach is referred to as heterogeneous non-orthogonal multiple access (H-NOMA), in contrast to the conventional NOMA techniques that involve users with homogeneous requirements and hence can be investigated through a standard multiple access channel. The study devises a communication-theoretic model that accounts for the heterogeneous requirements and characteristics of the three services. The concept of reliability diversity is introduced as a design principle that leverages the different reliability requirements across the services in order to ensure performance guarantees with non-orthogonal RAN slicing. This paper reveals that H-NOMA can lead, in some regimes, to significant gains in terms of performance tradeoffs among the three generic services as compared to orthogonal slicing.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, background): “On the other hand, new emerging use cases advocate a mixture of URLLC with eMBB and/or mMTC via slicing, in which a pool of shared resources (spectrum, computing power, and memory) is used [55]–[61].”
Edge Computing in 5G: A Review (2019, background): “g, ultra reliable low latency communication (URLLC), tactile internet) [5]–[7].”
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “The expected network reliability and availability is over 99.999% [8].”
URLLC-eMBB Slicing to Support VR Multimodal Perceptions over Wireless Cellular Systems (2018, methodology): “With a different SIC architecture that allows such a decoding attempt, (12) is regarded as the lower bound, as done in [17].”
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, background): “• Enhanced mobile broadband (eMBB) which aims to support data-intensive use cases such as virtual and augmented reality (V/AR), requiring high data rates across a wide coverage area [12].”
Computation Offloading Toward Edge Computing (2019, background): “Edge computing is a key technology toward 5G [24], and 5G accelerates the development of edge computing; 5G defines three usage scenarios: enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLCs), and massive machine-type communications (mMTCs) [28].”
Survey on Network Slicing for Internet of Things Realization in 5G Networks (2021, background): “2) Related Works: In [146], Popovski et al. discussed heterogeneous non-orthogonal sharing (H-NOMA) of RAN resources in uplink communications of a set of eMBB, mMTC and URLLC devices, which connects to a common base station in RAN slicing.”
Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends (2022, methodology): “Therefore, RSMA can be a low-complexity enabler for services with grant-free access and user intermittency, in which users are not always active and there is always an element of unpredictability in their activation, such as URLLC and mMTC [75].”
Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach (2020, methodology): “The work in [2] proposed a communication-theoretic model for eMBB, mMTC, and URLLC services considering traffic dynamics that are inherent to each individual service.”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “For channel decoding, the presence of fast-varying channels may make the first criterion hard to be satisfied in practice (unless channel estimation is made part of the learning process); while the stringent reliability requirements of some 5G services [29] may preclude the use of machine learning o”
A1 Resonators in 128° Y-cut Lithium Niobate with Electromechanical Coupling of 46.4% (2020, background): “THE emerging fifth-generation (5G) wireless communication is calling for unprecedented signal processing in different application scenarios [1], [2].”
Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks (2017, background): “Some works ([15], [16]) use information theoretic results to obtain expressions for the average eMBB rates under URLLC puncturing for various decoding schemes for uplink eMBB traffic punctured/superposed by URLLC users.”
A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies (2020, methodology): “In [34], the non-orthogonal based resource sharing scheme is used for supporting the heterogeneous services offered by eMBB, mMTC and uRLLC.”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “Most of the existing papers on network slicing either assumed that the required resources to guarantee the QoS of each service are known at the central controller [107] or considered a simpliﬁed model to formulate QoS constraints [110].”
Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come (2019, background): “6, and enables the integration of the reconfigurable meta-surfaces into the software-defined networking paradigm [5, 86].”