Evolutionary Game Theory Based Network Selection for Constrained Heterogeneous Networks

Sui, Nannan; Zhang, Dongmei; Zhong, Weizhi; Wu, Lianguo; Zhang, Zhensong

doi:10.1109/icisce.2015.170

Cited by 8 publications

(3 citation statements)

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“…Q-Learning convergence was also compared against RD in [108]. The model in [96] was reused in [145], adding further network constraints. It was assumed that network operators may want to set some limits on their RATs, e.g., the maximum number of accepted users, in order to maintain the QoS to predefined values.…”

Section: Application Of Evolutionary Games To Rat Selectionmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.

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Section: Application Of Evolutionary Games To Rat Selectionmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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“…Allocated bandwidth and price per connection are the parameters that are used for score calculation. The selection concept using evolutionary game in heterogeneous networks environment is also explained in the study of Sui et al In this, concept of population prevailing in this game model is discussed, and then, selection of network is done. This selection phenomenon is based on learning approach.…”

Section: Related Workmentioning

confidence: 99%

RAT selection for a low battery mobile device for future 5G networks

Adiwal

Singh

2019

Int J Communication

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Summary The growth of wireless communication toward fifth generation will lead to the existence of number of access technologies to provide seamless connectivity and form heterogeneous network environment. Earlier, there was single access technology to run applications, but 5G will have heterogeneous network environment and provide separate network for each application. As compared with 4G, 5G will provide increase in data rate, decrease in delay, increase in quality of service, and so on because of its various enabling technologies. Therefore, for each application, selection of best access network via its enabling technology is an important task. This selection is done either at user terminal side or at network operator side by combining preferences for network attributes and network parameters. In this paper, to enjoy 5G, selection is done in a heterogeneous networks environment for enabling technologies like device‐to‐device communication, spectrum sharing, enhancing quality of experience, energy efficiency, and so on. This selection is done via optimization techniques for a fixed duration video clip that is to be transmitted from a device running low in battery. The selection environment composed of UMTS, WLAN1, and WLAN2 as available networks. The simulation results show that the network selected for each enabling technology supports various features of 5G. Also, error analysis of selection results is done using confidence interval estimate at 90%, 92%, and 95% confidence level. From results obtained, it is seen that different optimization techniques used to access network for different enabling technologies (providing 5G features) prove to be useful for future 5G network.

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“…A centralized population evolution algorithm is proposed in Ref. [10] to implement the evolution game and address the network selection problem in constrained heterogeneous networks. Combined with analytic hierarchy process, Ref.…”

Section: Related Workmentioning

confidence: 99%