Applying ANFIS Model in Decision-making of Vertical Handover between Macrocell and Femtocell Integrated Network

[EN] One of the most challenging tasks in communication networks is to maintain seamless mobility and service continuity during a vertical handover. This paper provides the case of handover decision making between femtocell and macrocell integrated network considering several input parameters, namely SINR, bandwidth and energy consumption. We have simulated and proposed a vertical handover based on adaptive neuro-fuzzy inference system (ANFIS) to achieve a goal of having an intelligent handover and to predict the best destination network. The simulation results show that the approach based on ANFIS leads to a reduction of unnecessary handovers and a minimization of the energy consumption as compared to the existing approaches.Benaatou, W.; Latif, A.; Pla, V. (2019). Applying ANFIS Model in Decision-making of Vertical Handover between Macrocell and Femtocell Integrated Network. Journal of Telecommunication, Electronic and Computer Engineering. 11(1):57-62. http://hdl.handle.net/10251/159152S576211

Fuzzy Logic

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Analytical Review and Study on Various Vertical Handover Management Technologies in 5G Heterogeneous Network

In recent mobile networks, due to the huge number of subscribers, the traffic may occur rapidly; therefore, it is complex to guarantee the accurate operation of the network. On the other hand, the Fifth generation (5G) network plays a vital role in the handover mechanism. Handover management is a prominent issue in 5G heterogeneous networks. Therefore, the Handover approach relocates the connection between the user equipment and the consequent terminal from one network to another. Furthermore, the handover approaches manage each active connection for the user equipment. This survey offers an extensive analysis of 50 research papers based on existing handover approaches in the 5G heterogeneous network. Finally, existing methods considering conventional vertical handover management strategies are elaborated to improve devising effective vertical handover management strategies. Moreover, the possible future research directions in attaining efficient vertical handover management in a 5G heterogeneous network are elaborated

Advances in Reinforcement Learning

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Intelligent Circuits and Systems

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Multilayer perceptron neural network-based QoS-aware, content-aware and device-aware QoE prediction model : a proposed prediction model for medical ultrasound streaming over small cell networks

This paper presents a QoS-aware, content-aware and device-aware non-intrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the acceptable diagnostic quality through a device-aware adaptive video streaming mechanism. The proposed model is trained for an unseen dataset of input variables such as QoS, content features, and display device characteristics, to produce an output value in the form of m-QoE (i.e. MOS). The efficiency of the proposed model is validated through subjective tests carried by medical experts. The prediction accuracy obtained via the correlation coefficient and Root Mean-Square-Error (RMSE) indicates that the proposed model succeeds in measuring m-QoE closer to the visual perception of the medical experts. Furthermore, we have addressed the following two main research questions: (1) How significant is ultrasound video content type in determining m-QoE? and (2) How much of a role does the screen size and device resolution play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatio-temporal features, by including these features in the proposed prediction model, and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a novel subjective experiment of the ultrasound video sequences across multiple devices