Volume 60, Nº 10 (2017)

In the global scenario, a variety of wireless access networks are available. Different types of applications such as real time, nonreal time, and high bandwidth availability are used for heterogeneous wireless networks. Therefore, it is necessary for a service provider to make an appropriate connection support. For better performance, connections are to be exchanged among the different networks using seamless vertical handoff (VHO). The proposed algorithm shows the effect of optimization technique, which involves handoff decision process using vertical handoff triggering and selection of the network. The handoff triggering is initiated by using the received signal strength (RSS). In traditional method, handoff triggering is initiated by using RSS only. This method, modified weed optimization (M-WO) algorithm, reduces the unnecessary handoff by considering both RSS and velocity of the mobile node in handoff triggering. The parameters such as battery lifetime, handoff call dropping rate, load, dynamic weights adaptation and so on are to be considered individually or combined to make an effective network selection process. This paper highlights a novel effect ofM-WOalgorithm for decision making during the VHO. Our effort is to essentially optimize the system load, so that it reduces the handoff call dropping rate and the battery power consumption of the mobile node (MN). Weight of each QoS metrics is adjusted along with the networks changing conditions to trace the M-WO. Therefore, the novel VHO decision-making algorithm is superior to the existing SSF and OPTG methods. The simulation results show that the performance ofM-WOalgorithm is far better than SSF andOPTGmethods in terms of load, handoff call dropping rate and battery lifetime of MN.

The problem of digital signal recognition has been considered in conditions of deforming distortions of the waveform of these signals and additive Gaussian noise. A mathematical model for introducing deformations of the known or random waveform signals is proposed for synthesizing recognition algorithms. The model is based on introducing the nonlinear deformation operator as an operator of permutations with repetitions of elements of the initial discrete signal with addition of additive noise component caused by quantization errors of continuous deformation function. Two recognition algorithms were synthesized and investigated. The first is an optimal one based on the exact calculation of likelihood functions, and the second is a quasi-optimal algorithm based on using the Gaussian approximation of likelihood functions. These algorithms were simulated for different variants of the specified values of deforming distortions in the form of determinate functions and in the form of random function realizations. The experimental error probability was compared with its theoretical estimate at different values of signal-to-noise ratio.