Effect of mobility models on infrastructure based wireless networks

The tremendous demand is pushing the development of wireless mobile communications faster than ever before. Handoff management has widely been recognized as one of the most important and challenging problems for a seamless access to wireless network and mobile services. Mobility Models plays an important role in handoff management. In this paper, the effect of handoff procedure on the performance of random mobile nodes in wireless networks was investigated. Mobility of node is defined by various mobility models. Evaluating mobility models within an infrastructured network gives solution to performance measures like blocking probability, dropping probability to evaluate the performance of handoff algorithm. Handoff algorithm based on Absolute and Relative Measurement was used to examine the effect of Random Walk and Gauss Markov mobility models on performance of Infrastructure based Wireless Network. Keywords - Call blocking probability, Call dropping probability, Mobility model

A comparitive study on handover probability analysis for future HetNets

The need of wireless services increasing day by day due to the advancements in the field of wireless technology towards 5G for instant transferring the mails, messages and video calling without any interruption. In LTE and 5G wireless networks, major task is to provide seamless connection anywhere, anytime when the user may roam among Heterogeneous Wireless Networks (HetNets). To achieve proper mobility management among HetNets, handoff or hadover is required. Handover Probability is one of the metric to estimate the handover performance, which is a probability of Mobile Node to handover the present connection from the current base station to another base station or enode B. In this paper, handoff probability analysis is done for   multiple HetNets based on Handover Algorithm. To estimate this algorithm, bandwidth is considered as one of the key parameter. A comparative analysis of handover probability for two, three, four and five HetNets has been performed. The results can demonstrate that the variation of handover probability with respect to traffic load, threshold and bandwidth. It is observed that, as the number of wireless networks increases handover probability slightly increases with traffic load. These results are more significant to estimate further wrong decision handovers based on that Quality of Service (QoS) is evaluated in practical HetNets such as integration of LTE, Wi-Fi and WiMAX etc.