Network selection in wireless heterogeneous networks

Prabhavathi, P.; Nithyanandan, L.

doi:10.1109/iccsp.2013.6577074

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Results show convergence to stable configurations in different scenarios, including underloaded vs. congested RATs and different service costs. Bush-Morsteller and Boltzmann-Gibbs CODIPAS-RL are compared in a WiMAX/WLAN scenario in [236], where the latter shows faster convergence. Moreover, Multiplicative Weighted Imitative CODIPAS-RL is used to benchmark the network-assisted solution in [93] (Section VI-D1), showing low performance in terms of convergence speed, per-user switchings, and system utility compared to other learning schemes, that however exploit more information and network assistance.…”

Section: ) Stochastic Learning Automata (Sla)mentioning

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: ) Stochastic Learning Automata (Sla)mentioning

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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“…Bush Mosteller based CODIPAS-RL is a stochastic model of reinforcement learning where users decide which action to take stochastically: each user's strategy is defined by the probability of undertaking each of the two actions available to them. After every user has selected an action according to their probability, every user receives the corresponding utility and revises their strategy [6,11,12]. The action of each user is determined based on (1)…”

Section: Bush Mosteller Based Codipas-rlmentioning

confidence: 99%

User Centric Network Selection in Wireless HetNets

Nithyanandan¹,

Bharathi²,

Prabhavathi³

2013

IJWMN

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The future generation wireless networks are expected to beheterogeneous networksconsisting of UMTS, WLAN, WiMAX, LTE etc. A heterogeneous network provides users with different data rate and Quality of Service (QoS). Users of future mobile networks will be able to choose from different radio access technologies. These networks vary widely in service capabilities such as coverage area, bandwidth and error characteristics. Network selection is a challenging task in heterogeneous networks and will influence the performance metrics of importance for both service provider and subscriber.This paper analysesuser centric network selection based on QoE (Quality of Experience) which include both technical and economical aspects of the user. WLAN-WiMAX-UMTS networks are integrated and the network selection for the integrated network is performed using game theory based network selection algorithm.

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“…System performance should be guaranteed by a network selection scheme while satisfying subjective requirements such as user preferences. Although bit error rate (BER) was evaluated in [15] and packet loss rate (PLR) and average download cost were simulated in [7], there is a lack of more comprehensive simulations showing the actual impact of network selection on system performance such as average user equipment (UE) SINR and spectral efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…The network selection problem has been modeled using Markov chain [2] [3][4] [5], utility theory [6] [7], fuzzy logic theory [8] [9][10], game theory [11], multiple attribute decision making (MADM) [9][12] [13] [14], reinforcement learning [15] and fittingness-factor based schemes [16]. Markov chain and game theory methods provide better precision at the cost of higher complexity and slower decision making.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical MADM-based network selection scheme for system performance enhancement

Chu

Zhang

2014

2014 IEEE International Conference on Communication Systems

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In a heterogeneous wireless network consisting of macrocells and femtocells, it is important for mobile terminals to decide appropriate cell associations when both radio access technologies (RATs) are available. This problem is also known as network selection. Nowadays mobile users are distinct in terms of quality of service (QoS) demand, moving speed and other service preferences, while the conventional maximum signal-to-interference-plus-noise-ratio (SINR) based cell association schemes are not able to jointly consider these different requirements. In this paper, we propose a hierarchical cell association algorithm based on the multiple attribute decision making (MADM) method but with a simplified process and fewer carefully chosen attributes to lower computational complexity. The performance in terms of user's average wideband SINR, spectral efficiency and load balance is evaluated through simulations. The results show that the average wideband SINR and spectral efficiency are improved for 60%-80% of users as compared to the max-SINR scheme. Load balancing is also achieved.

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Network selection in wireless heterogeneous networks

Cited by 3 publications

References 8 publications

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

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

User Centric Network Selection in Wireless HetNets

A hierarchical MADM-based network selection scheme for system performance enhancement

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