Vertical Handover Decision Algorithm Using Ants’ Colonies for 4G Heterogeneous Wireless Networks

Abstract

With the development of 4G mobile telecommunication systems, providing users with the convenience of a seamless continuous connection is not enough anymore. Users want to be connected through the best available network with the best quality of service. It is necessary to have a good decision-making algorithm which decides whether it is necessary to perform handoff to another network, the best network to vertically perform handoff to, and the right time to initiate the handover. This paper proposes a new approach in which mobile terminals (MTs) continuously analyze the network and keep a database of the best available networks. The concept is based on QoS aware ant colony built on the vertical handoff mechanism that uses an updated version of ants’ colony optimization decision algorithm (ACOR), the dynamic and static factors such as RSS, the cost of service, bandwidth, the velocity of MT, the power consumption and security, and the module for predicting the traveling distance within an IEEE 802.11 WLAN cell. Simulation results show that we can not only meet the individual needs of users in terms of QoS, but also improve the whole system performance by reducing the number of handover failures and unnecessary handover instances by up to 95%.

RRH-Sector selection and load balancing based on MDP and dynamic RRH-Sector-BBU mapping in C-RAN

“…where the authors indicate possible underutilization of network resources, lack of fairness, and inappropriate load balancing in the network. The RSS metric is conventionally used for selection and handover (Benaatou et al, 2017;El Fachtali et al, 2016), but some authors (Mahardhika et al, 2015;Miyim et al, 2013) argue that it is only convenient to start the handover and then adding some other QoS criteria is needed for better results. Therefore, to maximize the utilization in the C-RAN network, a decision for RRH-Sector selection should consider the requirements and constraints of the user and the operator.…”

Cell Selection Mechanism Based on Q-learning Environment in Femtocell LTE-A Networks

“…Several mobility management algorithms have been proposed in [13][14][15][16][17] to predict the target cell. The authors of [13] suggest an effective measurement scheme and network investigation for user equipment mobility from macrocell to femtocell (hand-in/handover scenario).…”

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“…Furthermore, to decrease the number of needless handovers and the number of handover failures, the authors in [16] proposed ant colony optimization for the handover stage. The proposed scheme uses several input factors along with RSS, such as prediction of traveling distance, UE velocity, cost of service, and bandwidth.…”