Results 91 to 100 of about 9,462 (229)
Performance of Spatially Diverse URLLC and eMBB Traffic in Cell Free Massive MIMO Environments
IEEE Transactions on Network and Service Management5G networks are designed to support both high data rate eMBB services, and low-latency high-reliability URLLC services. Multiplexing these two services on the same wireless frequency band leads to a challenging radio resource allocation problem due to ...
U. Bucci, D. Cassioli, A. Marotta
semanticscholar +1 more source
IET Communications, Volume 19, Issue 1, January/December 2025.This paper presents SAMA, a simulator for 5G Radio Access Networks (RAN), designed to evaluate the performance of different network configurations. The diagram illustrates the simulator's workflow: from inputs such as base station (BS) and user equipment (UE) arrangements, SAMA simulates resource allocation and scheduling, considering interference.
Christian Fragoas F. Rodrigues +2 more
wiley +1 more source
Slicing Resource Allocation for eMBB and URLLC in 5G RAN
Wireless Communications and Mobile Computing, 2020 This paper investigates the network slicing in the virtualized wireless network. We consider a downlink orthogonal frequency division multiple access system in which physical resources of base stations are virtualized and divided into enhanced mobile broadband (eMBB) and ultrareliable low latency communication (URLLC) slices.
Tengteng Ma +4 more
openaire +1 more source
Joint Link Adaptation and Scheduling for 5G Ultra-Reliable Low-Latency Communications
IEEE Access, 2018 This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared channel.
Guillermo Pocovi +2 more
doaj +1 more source
Random Access Protocols for Massive MIMO
, 2017 5G wireless networks are expected to support new services with stringent requirements on data rates, latency and reliability. One novel feature is the ability to serve a dense crowd of devices, calling for radically new ways of accessing the network ...
Björnson, Emil +4 more
core +1 more source
Exploring the Potential of AI in Network Slicing for 5G Networks: An Optimisation Framework
IET Communications, Volume 19, Issue 1, January/December 2025.Our approach is to incorporate precise programming of segment routing over IPv6 (SRv6) identifiers, allowing dynamic assignment of SIDs based on predicted slice types, accurately distinguishing up to 40,000 eMBB slices and separating uRLLC and mMTC slices with high proficiency.
Zeina Boufakhreddine +4 more
wiley +1 more source
Machine learning approach of multi‐RAT selection for travelling users in 5G NSA networks
IET Networks, Volume 14, Issue 1, January/December 2025.The authors present RAT selection algorithm for efficient resource allocation for public bus travelling user(s), utilising live data from multiple 5G NSA base stations. Models of the supervised machine learning algorithms: SVM, DNN and XGBoost were deployed for implementation to predict or select the appropriate RAT between 4G and 5G RATs.
Nurudeen O. Salau +2 more
wiley +1 more source
Competitive Advantage of URLLC vs. eMBB for Supporting Timeliness-relevant Services
SSRN Electronic Journal5G specifications promise a common and flexible-enough network infrastructure capable of satisfying diverse requirements of both current and future use cases. Two service types standardized in 5G are eMBB, without stringent delay guarantee, and URLLC, with stringent delay guarantee.
Guijarro, Luis +2 more
openaire +2 more sources
Rate-Flexible Fast Polar Decoders
, 2019 Polar codes have gained extensive attention during the past few years and recently they have been selected for the next generation of wireless communications standards (5G).
Condo, Carlo +3 more
core +1 more source
IoT‐5G and B5G/6G resource allocation and network slicing orchestration using learning algorithms
IET Networks, Volume 14, Issue 1, January/December 2025.In this article, the challenges related to the evolution of 5G and B5G/6G networks are examined more closely. The authors then primarily focus on machine learning solutions for resource allocation in 5G and B5G/6G networks. The requirements for dynamic network slicing orchestration are also analysed.
Ado Adamou Abba Ari +5 more
wiley +1 more source