A Matching Game Solution for Optimal RAT Selection in 5G Multi-RAT HetNets

Anany, M. G.; Elmesalawy, Mahmoud M.; Din, Eman Serag El

doi:10.1109/uemcon47517.2019.8993013

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The authors in (Anany, Elmesalawy, & El Din, 2019) proposed a two scenario RAT selection considering a long term evolution (LTE) and a wireless local area network (WLAN) using a matching game algorithm. While this approach produced some interesting results, the scope was very limited and does not reflect the current multi-tier network adopted in 5G.…”

Section: Literature Reviewmentioning

confidence: 99%

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

Mourice Otieno Ojijo,

Dismas Ombuya,

Andrew Kipkebut

et al. 2021

Kabarak j. res. innov.

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The expected massive connectivity in 5G wireless network is bound to become a challenge to service providers. Many services over the 5G network will be aligned to a particular radio access network (RAN). As a result admitting a service based user to a particular RAN will depend on the most efficient radio access technology selection (RAT). This is because 5G network will adopt multi-tier radio access networks ranging from high power macro base stations to extremely low power Bluetooth connectivity. Selection of a service oriented RAT is critical because some wireless services have superior quality of service under certain RATs. Maintaining efficient RAT selection by network operators will improve power allocation efficiency, bandwidth allocation efficiency and operation expenditure. The complexity of associating a RAT to service based user while considering network state such as service packet size, the turnaround time, the power allocation has not been fully explored. In this paper we propose a reinforcement learning approach to user admission based on efficient RAT selection considering wireless services in a cross tier wireless radio access network domain. The proposed algorithm is expected to improve RAT selection efficiency while minimizing the computation complexity. We perform extensive simulation using Python dynamic libraries and present our results alongside existing approaches

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Section: Literature Reviewmentioning

confidence: 99%

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

Mourice Otieno Ojijo,

Dismas Ombuya,

Andrew Kipkebut

et al. 2021

Kabarak j. res. innov.

View full text Add to dashboard Cite

show abstract

Game theory approaches for Radio Access Technology Selection Algorithm: A Review

Ariba,

Adeyemo,

Ojo

2024

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)

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Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

Ariba,

Ojo,

Adeyemo

2024

AJERD

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In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

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A Matching Game Solution for Optimal RAT Selection in 5G Multi-RAT HetNets

Cited by 3 publications

References 18 publications

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

A Reinforcement Learning Approach to Service Based User Admission in a Multi-Tier 5G Wireless Networks

Game theory approaches for Radio Access Technology Selection Algorithm: A Review

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

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