Online Radio Access Technology Selection Algorithms in a 5G Multi-RAT Network

Roy, Arghyadip; Chaporkar, Prasanna; Karandikar, Abhay; Jha, Pranav

doi:10.1109/tmc.2021.3096968

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Moreover, it facilitates indepth investigations into various placement and replication strategies, a pivotal aspect often addressed in academic literature through simulations and theoretical analyses [55]- [58]. Additionally, it opens possibilities for refining solutions in 5G multi-RAT environments, allowing seamless channel selection optimization [59], [60]. Finally, it supports the comparison of different mmWave RAT technologies, offering valuable insights for their application in edge computing.…”

Section: Testbed Potentialmentioning

confidence: 91%

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Baruffa,

Detti,

Rugini

et al. 2024

IEEE Open J. Commun. Soc.

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The seamless integration of multiple radio access technologies (multi-RAT) and cloud/edge resources is pivotal for advancing future networks, which seek to unify distributed and heterogeneous computing and communication resources into a cohesive continuum system, tailored for mobile applications. Many research projects and focused studies are proposing solutions in this area, the impact of which is undoubtedly increased by moving from theoretical and simulation studies to experimental validations. To this aim, this paper proposes a testbed architecture that combines contemporary communication and cloud technologies to provide microservice-based mobile applications with the ability to offload part of their tasks to cloud/edge data centers connected by multi-RAT cellular networks. The testbed leverages Kubernetes, Istio service mesh, OpenFlow, public 5G networks, and IEEE 802.11ad mmWave (60 GHz) Wi-Fi access points. The architecture is validated through a use case in which a ground robot autonomously follows a moving object by using an artificial intelligence-driven computer vision application. Computationally intensive navigation tasks are offloaded by the robot to microservice instances, which are executed on demand within cloud and edge data centers that the robot can exploit during its journey. The proposed testbed is flexible and can be reused to assess communication and cloud innovations focusing on multi-RAT cloud continuum scenarios.

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Section: Testbed Potentialmentioning

confidence: 91%

AI-Driven Ground Robots: Mobile Edge Computing and mmWave Communications at Work

Baruffa,

Detti,

Rugini

et al. 2024

IEEE Open J. Commun. Soc.

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“…In [15], Roy et al have proposed online radio access technology selection algorithms in a 5G Multi-RAT Network. The authors have considered a heterogenous wireless network consisting of a 5G New Radio and a WiFi network with the objective of maximizing the total system throughput subject to constraints on the blocking probability.…”

Section: Related Workmentioning

confidence: 99%

“…In the LTE network, BL serves the MBB service while in the NSA 5G network, BN serves the MBB/EMBB service. In the SA 5G network, BS is divided into two bandwidth parts (BWPs), denoted as BS1 and BS2, such that BS = BS1 + BS2 [15]. Thus, in the SA 5G network, BS1 serves the MBB/eMBB service while the BS2 serves the uRLLC services.…”

Section: System Modelmentioning

confidence: 99%

Investigation of Network Selection Decisions for Mixed Deployment of SA and NSA 5G Network

Falowo

2024

Preprint

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As migration from 4G network to 5G network gains momentum across the world, network operators are faced with different options when migrating from the 4G to a unified 5G network. These options have been broadly classified as standalone (SA) and non-standalone (NSA) options. While an operator can migrate from the 4G network to the 5G network in one step, using the SA 5G deployment option, for most network operators, the migration from the 4G network to a unified 5G network is a multistage process, starting with an NSA 5G deployment option. This paper investigates network selection decisions for a mixed deployment of NSA and SA 5G network, along the migration path to the realization of a unified 5G network. To the best of our knowledge, this is the first paper investigating network selection decisions in a 5G network with mixed deployment of NSA and SA options. A Markov model has been developed to investigate the blocking of MBB, eMBB, and uRLLC services from different categories of users in the network, based on their equipment capabilities and service subscription profiles. Moreover, the developed model has been used to investigate the effect of users’ equipment capability on average utilization of the heterogeneous network. Simulation results show that users’ equipment capability and users’ service subscription profiles significantly affect service blocking probabilities and average utilization in the heterogenous network. The study underscores the importance of upgrading users’ equipment and users’ subscription profiles before reaping the connection-level QoS benefit of deploying the 5G network.

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