Performance evaluation of softer vertical handovers in multiuser heterogeneous wireless networks

Bazzi, Alessandro; Masini, Barbara M.; Dardari, Davide

doi:10.1007/s11276-015-1140-8

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…Step 2: calculate the weight of each network attribute in the service type scenario. According to [19], B is a positive reciprocal matrix which has multiple eigenvalues and eigenvector pairs (λ, V):…”

Section: Wifi Offloading Algorithm Based On Q-learning and Madmmentioning

confidence: 99%

WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

Sun

Zhu

2019

Mobile Information Systems

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This paper proposes a WiFi offloading algorithm based on Q-learning and MADM (multiattribute decision making) in heterogeneous networks for a mobile user scenario where cellular networks and WiFi networks coexist. The Markov model is used to describe the changes of the network environment. Four attributes including user throughput, terminal power consumption, user cost, and communication delay are considered to define the user satisfaction function reflecting QoS (Quality of Service), and Q-learning is used to optimize it. Through AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) in MADM, the intrinsic connection between each attribute and the reward function is obtained. The user uses Q-learning to make offloading decisions based on current network conditions and their own offloading history, ultimately maximizing their satisfaction. The simulation results show that the user satisfaction of the proposed algorithm is better than the traditional WiFi offloading algorithm.

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Section: Wifi Offloading Algorithm Based On Q-learning and Madmmentioning

confidence: 99%

WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

Sun

Zhu

2019

Mobile Information Systems

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“…The taxonomy on handover strategies (hard, semi-soft, soft, softer vertical handovers) is recalled in [1] where the authors propose a softer vertical handover and evaluate its performance under different assumptions. Their results demonstrate the feasibility of softer vertical handover, with an achievable throughput as high as the sum of that provided by the single radio access technology, but they comment on the not negligible modifications required at some layers of the protocol stack.…”

Section: A Related Workmentioning

confidence: 99%

“…To this aim we introduce the cost function G(n, v, k), that is the cost to access the v-th network starting from the n-th at time index k. More, we account for the memory weight to be given to the past (through index l starting from K time periods before with the respect to the current, k) with the d[l] term. 1 We have then at time period k the overall cost is described by the following Bellman equation [18, Ch. 5]:…”

Section: Figure 2 Graphical Interpretation Of Available Network and U...mentioning

confidence: 99%

A Multi-Layer Parametric Approach to Maximize the Access Probability of Mobile Networks

et al. 2016

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Next-generation mobile networks (5G) are defined to provide access in the framework of heterogeneous systems where it is crucial to have “always on” and “everywhere connectivity” capabilities. This is of fundamental importance even in 4G mobile systems, down to 3G and also Wi-Fi and WiMAX. In order to guarantee access to users with handheld devices equipped with multiple radio interfaces, an automated and reconfigurable tool for selecting the best network to be connected with is needed. This should be achieved by avoiding service outages. Current vertical handovers, i.e., switching from a network to another, are essentially based on power received levels and often do not avoid temporary service outages. We propose in this paper a procedure to access mobile networks by sensing multiple performance parameters related to networks available in the considered area. We target at maximizing the probability of accessing the wireless medium despite the technology used. We develop an algorithm, based on dynamic programming, able to select the most suitable network. We present the performance of the proposed algorithm both on the basis of computer simulations and on tests performed in an Arduino-based hardware platform

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“…У більшості з відомих наукових робіт у процесі оптимального вибору мережі радіодоступу в гетерогенному середовищі розглядаються лише декілька параметрів, які не дають змоги динамічно адаптувати правила прийняття рішень щодо хендоверу з урахуванням мінливих вимог користувачів щодо якості надання послуг [10][11][12]. Зазвичай різні послуги та додатки управляються за допомогою набору параметрів QoS (наприклад, втрат, затримки та джитера пакетів) [13].…”

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Quality of service management for an intent-based heterogeneous network using mobile QoE application

Beshley¹,

Prislupskiy²,

Beshley³

2021

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Traditional Service Level Agreement (SLA) based on the quality of service (QoS) management methods are insufficient to ensure quality-related contracts between service providers and users. This article proposes a user-centric method for QoS management in heterogeneous mobile networks. Based on a new QoE metric on a scale of 1 to 5, this method considers the commercial value of electronic services to end-users. With this approach, the configuration and functionality of the network automatically change depending on the requirements of the end-users. The work proposes a conceptual model for constructing an intent-based software-defined heterogeneous network, which effectively manages shared resources and adapts to users’ needs. A prototype of a mobile and operator application for adaptive client-oriented service delivery in a heterogeneous network has been developed, which makes it possible to obtain the ordered QoE based on the feedback between the user and the network operator. Using this approach will allow network operators to provide individualization of service users with a certain level of QoS by analyzing their estimates of QoE (ordered through the developed mobile application). And the use of machine learning algorithms will allow to react to unfavorable combinations of values of quality indicators and prevent the situation when the user is not satisfied with the quality of services received for adaptive prediction of the moment of network reconfiguration. We propose a method for managing the QoS provision in a heterogeneous wireless network using Big Data technology and a mobile QoE application, which considers and analyzes the estimates of the ordered QoE and allows users QoS improvement according to the demand. It is demonstrated that using the proposed method in a heterogeneous wireless network allows reducing the number of dissatisfied users with the quality of service by up to 60% using an experimental study.

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Performance evaluation of softer vertical handovers in multiuser heterogeneous wireless networks

Cited by 8 publications

References 43 publications

WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

WiFi Offloading Algorithm Based on Q-Learning and MADM in Heterogeneous Networks

A Multi-Layer Parametric Approach to Maximize the Access Probability of Mobile Networks

Quality of service management for an intent-based heterogeneous network using mobile QoE application

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