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 fuzzy-based handover system for avoiding ping-pong effect in wireless cellular networks

Many handover algorithms are proposed in the literature. However, to make a better handover and keep the QoS in wireless networks is very difficult. In this paper, we propose a new handover system based on fuzzy logic. The proposed system uses 3 parameters for handoff decision: the change of signal strength of the present Base Station (BS), signal strength from the neighbor BS, and the distance between Mobile Station (MS) and BS. The performance evaluation via simulations shows that proposed system can avoid ping-pong effect and has a good handover decisionPeer ReviewedPostprint (published version

Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

To provide the seamless mobility in heterogeneous wireless networks two significant methods, simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect

A comparison study of two fuzzy-based handover systems for avoiding ping-pong effect in Wireless Cellular Networks

Wireless mobile networks and devices are becoming increasingly popular to provide users the access anytime and anywhere. The mobile systems are based on cellular approach and the area is covered by cells that overlap each other. Many handover algorithms are proposed in the literature. However, to make a better handover and keep the QoS in wireless networks is very difficult task. For this reason, new intelligent algorithms should be implemented to deal with this problem. In this paper, we carried out a comparison study of two handover systems based on fuzzy logic. We implement two fuzzy-based handover systems (FBHS). We call them FBHS1 and FBHS2. The performance evaluation via simulations shows that FBHS2 has better behavior than FBHS2 and can avoid ping-pong effect in all simulation cases.Peer ReviewedPostprint (published version

Implementation and performance evaluation of two fuzzy-based handover systems for wireless cellular networks

Wireless mobile networks and devices are becoming increasingly popular to provide users the access anytime and anywhere. We are witnessing now an unprecedented demand for wireless networks to support both data and real-time multimedia traffic. The wireless mobile systems are based on cellular approach and the area is covered by cells that overlap each other. In mobile cellular systems the handover is a very important process. Many handover algorithms are proposed in the literature. However, to make a better handover and keep the QoS in wireless networks is very difficult task. For this reason, new intelligent algorithms should be implemented to deal with this problem. In this paper, we carried out a comparison study of two handover systems based on fuzzy logic. We implement two Fuzzy-Based Handover Systems (FBHS) called FBHS1 and FBHS2. The performance evaluation via simulations shows that FBHS2 has better behavior than FBHS1 and can avoid ping-pong effect in all simulation cases.Peer ReviewedPostprint (published version

Handover in Mobile Wireless Communication Network - A Review

Mobility is the characteristics of mobile communication that makes it irresistible by all and sundry. The whole world is now engaging in wireless communication as it provides users\u27 ability to communicate on-the-go. This is achieved by transferring users from a radio network to another. This process is called handover. Handover occurs either by cell crossing or by deterioration in signal quality of the current channel. The continuation of an active call is a critical characteristic in cellular systems. Brief overview of handover, handover type, commonly used handover parameters, some methods employed in the literature and we present the convergent point for furtherance in the area of mobile wireless communication Handover

Mobility Analysis and Management for Heterogeneous Networks

The global mobile data traffic has increased tremendously in the last decade due to the technological advancement in smartphones. Their endless usage and bandwidth-intensive applications will saturate current 4G technologies and has motivated the need for concrete research in order to sustain the mounting data traffic demand. In this regard, the network densification has shown to be a promising direction to cope with the capacity demands in future 5G wireless networks. The basic idea is to deploy several low power radio access nodes called small cells closer to the users on the existing large radio foot print of macrocells, and this constitutes a heterogeneous network (HetNet). However, there are many challenges that operators face with the dense HetNet deployment. The mobility management becomes a challenging task due to triggering of frequent handovers when a user moves across the network coverage areas. When there are fewer users associated in certain small cells, this can lead to significant increase in the energy consumption. Intelligently switching them to low energy consumption modes or turning them off without seriously degrading user performance is desirable in order to improve the energy savings in HetNets. This dynamic power level switching in the small cells, however, may cause unnecessary handovers, and it becomes important to ensure energy savings without compromising handover performance. Finally, it is important to evaluate mobility management schemes in real network deployments, in order to find any problems affecting the quality of service (QoS) of the users. The research presented in this dissertation aims to address these challenges. First, to tackle the mobility management issue, we develop a closed form, analytical model to study the handover and ping-pong performance as a function of network parameters in the small cells, and verify its performance using simulations. Secondly, we incorporate fuzzy logic based game-theoretic framework to address and examine the energy efficiency improvements in HetNets. In addition, we design fuzzy inference rules for handover decisions and target base station selection is performed through a fuzzy ranking technique in order to enhance the mobility robustness, while also considering energy/spectral efficiency. Finally, we evaluate the mobility performance by carrying out drive test in an existing 4G long term evolution (LTE) network deployment using software defined radios (SDR). This helps to obtain network quality information in order to find any problems affecting the QoS of the users

Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications