User Demand Wireless Network Selection Using Game Theory

Rajesh, L.; Bagan, K. Bhoopathy; Ramesh, B.

doi:10.1007/978-981-10-2999-8_4

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The thermal noise can be computed as (10) and the interference can be computed as (12). Using the Shannon formula (considering that this time the bandwidth is the total bandwidth of the satellite AP since the TDMA utilises all the bandwidth only for a certain amount of time), one has that the bitrate obtainable by a single block of symbols is 𝑟 𝑖,𝑝 = 𝑏𝐵 log 2 (1 + 𝑆𝐼𝑁𝑅 𝑖,𝑝 ), (15) where 𝑏 is the ratio between the number of symbols in a single block and the total number of symbols of the satellite AP. The number of blocks to be allocated for a requested bitrate 𝑏 𝑝𝑘 𝑖 from UE 𝑖 is then computed as 𝑛 𝑖,𝑝 𝑏𝑙𝑜𝑐𝑘𝑠 = ⌈(𝑏 𝑝𝑘 𝑖 𝑟 𝑖,𝑝 ⁄ )⌉…”

Section: Satellite Resource Allocation Descriptionmentioning

confidence: 99%

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

et al. 2022

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This paper proposes a Deep-Q-Network (DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov Decision Process (MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G, and for this reason the present work considers a realistic network model that takes into account path-loss models and intra-RAT (Radio Access Technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved thanks to the DQN algorithm with respect to a classic Reinforcement Learning algorithm and baseline approaches in terms of connection-flows' acceptance, resource allocation and load balancing.

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Section: Satellite Resource Allocation Descriptionmentioning

confidence: 99%

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

et al. 2022

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“…Salih et al (2016) modelled the network selection problem as a non-cooperative game to optimise the vertical handover decision in a heterogeneous network. Authors in (Rajesh et al 2017), adopted a game theory based network selection scheme that guarantees QoS at reasonable cost and high revenues for access network. Authors in (Goyal et al 2020), proposed a network selection scheme based on non-cooperative game theory for heterogeneous network that maximises user QoE and network revenues.…”

Section: Introductionmentioning

confidence: 99%

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

Priya¹,

Malhotra²

2021

J Ambient Intell Human Comput

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The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

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A Hybrid MADM Algorithm Based on Attribute Weight and Utility Value for Heterogeneous Network Selection

Huang

Zhang

2018

J Netw Syst Manage

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User Demand Wireless Network Selection Using Game Theory

Cited by 4 publications

References 16 publications

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network

A Hybrid MADM Algorithm Based on Attribute Weight and Utility Value for Heterogeneous Network Selection

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