Towards realization of the ABC vision: A comparative survey of Access Network Selection

“…More information on this general research problem can be found, e.g. from the survey papers [7][8][9][10][11][12]. The proposed algorithms are based on a multitude of different techniques, such as neural networks, reinforcement learning (RL), fuzzy logic, Bayesian network, game theory, multi-criteria decision-making and analytical hierarchical process.…”

Section: Related Workmentioning

confidence: 99%

User‐centric versus network‐centric load balancing: How to provide good QoE for gold users

Mämmelä

Mannersalo

2015

Int J Network Mgmt

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Summary The predicted increase in mobile data traffic and in the number of mobile users poses challenges for network operators. Future mobile networks should support traffic volumes of several orders of magnitude larger than today. This may create situations in which the transmission capacity of the network is inadequate for the traffic demand. Intelligent and robust network management methods are thus needed to handle the upcoming changes. Because quite often the bottleneck lies in the wireless part of the network, it is essential to have methods in place to select appropriate wireless access points for the users or their applications. This can be performed either by a central entity or in a distributed manner in which the intelligence is placed in the user equipment or hybrid combinations of those. The selection criteria may vary from selfish optimization of a single user's quality of experience to global network load balancing and class‐wise prioritization. This work explores the wireless access selection scheme from a system perspective in scenarios where users of different subscription classes are using progressive video streaming. Evaluation is carried out between a Q‐learning based user‐centric algorithm, a user class aware network‐centric algorithm and a setting without handovers. Simulation results show that both user‐centric and network‐centric algorithms are able to improve the network performance and the quality of experience of the users. The gains of the network‐centric algorithm are more evident during high congestion. In a lightly congested network, the distributed selfish optimization is also qualitatively a good solution for gold users. Copyright © 2015 John Wiley & Sons, Ltd.

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“…There are many proposals proposed by researchers about ANS, e.g. [4,5]; however, the proposed ANS schemes lack unity, while a number of issues still need to be resolved such as the goals discrepancy between user centric and network centric, where in user centric scheme the goal is how to get best connection anywhere anytime regardless of network operation complexities associated with this which matters from operator's perspective [6]. Although there are many approaches that have been presented to provide seamless VHO [2,3,[11][12][13][14][15][16], we will consider three of them [3, 11 and 12] which have been evolved from each other such that the recent approach of them [3] combined between MIH and ANDSF mechanisms.…”

Section: Related Workmentioning

confidence: 99%