Performance improvement of vertical handoff algorithms for QoS support over heterogenuous wireless networks

During the vertical handoff procedure, handoff decision is the most important step that affects the normal working of communication. An incorrect handoff decision or selection of a non-optimal network can result in undesirable effects such as higher costs, poor service experience, degrade the quality of service and even break off current communication. The objective of this paper is to determine the conditions under which vertical handoff should be performed in heterogeneous wireless networks. In this paper, we present a comprehensive analysis of different vertical handoff decision algorithms. To evaluate tradeoffs between their performance and efficiency, we propose two improved vertical handoff decision algorithm based on Markov Decision Process which are referred to as MDP_SAW and MDP_TOPSIS. The proposed mechanism assists the terminal in selecting the top candidate network and offer better available bandwidth so that user satisfaction is effectively maximized. In addition, our proposed method avoids unbeneficial handoffs in the wireless overlay networks

Modeling Seamless Vertical Handovers in Heterogeneous Wireless Networks

Vertical handover in heterogeneous wireless networks provides customers with better Quality of Service (QoS) experience. For seamless handover, timely initiation of handover process plays a key role. Various vertical handover management protocols have been proposed and standardized to support mobility across heterogeneous networks. In Media Independent Handover (MIH) based schemes, distributed handover decision is made via certain predefined triggers that consider user context. In this paper, we present a comprehensive review of the modeling techniques used during management of vertical handover. We have also defined a novel architecture, HRPNS: Handoff Resolving and Preferred Network Selection module enabling vertical handover that ensures QoS. The construction of HRPNS module involves integration of fuzzy logic and Markov Decision Process (MDP) for providing precise decision of handover

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.

Regressive Prediction Approach to Vertical Handover in Fourth Generation Wireless Networks

The over increasing demand for deployment of wireless access networks has made wireless mobile devices to face so many challenges in choosing the best suitable network from a set of available access networks. Some of the weighty issues in 4G wireless networks are fastness and seamlessness in handover process. This paper therefore, proposes a handover technique based on movement prediction in wireless mobile (WiMAX and LTE-A) environment. The technique enables the system to predict signal quality between the UE and Radio Base Stations (RBS)/Access Points (APs) in two different networks. Prediction is achieved by employing the Markov Decision Process Model (MDPM) where the movement of the UE is dynamically estimated and averaged to keep track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The performances of various handover approaches influenced by different metrics (mobility velocities) were evaluated. The results presented demonstrate good accuracy the proposed method was able to achieve in predicting the next signal level by reducing the total handover latency

A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks

Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on handoff blocking probabilities as Quality of Service constraints. However, this method is too time-consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings, where a value of “1” in the position i (i=1,…m) of a decision string stands for the decision of accepting a call in class-i; a value of “0” in the position i of the decision string stands for the decision of rejecting a call in class-i. The coded binary strings are feed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity

Efficient radio resource management in next generation wireless networks

The current decade has witnessed a phenomenal growth in mobile wireless communication networks and subscribers. In 2015, mobile wireless devices and connections were reported to have grown to about 7.9 billion, exceeding human population. The explosive growth in mobile wireless communication network subscribers has created a huge demand for wireless network capacity, ubiquitous wireless network coverage, and enhanced Quality of Service (QoS). These demands have led to several challenging problems for wireless communication networks operators and designers. The Next Generation Wireless Networks (NGWNs) will support high mobility communications, such as communication in high-speed rails. Mobile users in such high mobility environment demand reliable QoS, however, such users are plagued with a poor signal-tonoise ratio, due to the high vehicular penetration loss, increased transmission outage and handover information overhead, leading to poor QoS provisioning for the networks' mobile users. Providing a reliable QoS for high mobility users remains one of the unique challenges for NGWNs. The increased wireless network capacity and coverage of NGWNs means that mobile communication users at the cell-edge should have enhanced network performance. However, due to path loss (path attenuation), interference, and radio background noise, mobile communication users at the cell-edge can experience relatively poor transmission channel qualities and subsequently forced to transmit at a low bit transmission rate, even when the wireless communication networks can support high bit transmission rate. Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed. The performance of proposed ATMA CAC scheme is investigated and compare it with the traditional CAC scheme. The ATMA scheme exploits the mobility events in the highspeed mobility communication environment and the calls (new and handoff calls) generation pattern to enhance the QoS (new call blocking and handoff call dropping probabilities) of the mobile users. The numbers of new and handoff calls in wireless communication networks are dynamic random processes that can be effectively modeled by the Continuous Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed