A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model

Handover usually deals with the mobility of the end users in a mobile network to assure about the ongoing session of a user. It is observed that frequent handover results in call dropping due to latency. In order to overcome this issue, a fuzzy based handover decision scheme for mobile devices using a predictive model is proposed. First, an MFNN (Multi-layer Feed Forward Network) is used to determine the next cell of the user along with best hand off time. To obtain the best access network, multiple-attribute Access Network Selection Function (ANSF) is used. The fuzzy rule is applied by considering the parameter data rate, reliability, signal strength, battery power and mobility as input and the output obtained is the optimal network. The proposed scheme selects the best access network and enhances the quality of services.

Distributed detection basis on using soft decision scheme both in local sensors and in fusion center

The paper considers distributed detection problem basis on using soft decision scheme both in the local sensors and in the fusion center (FC). The algorithm for making soft decisions when receiving data from local sensors in the fusion center and its performance characteristics, which are necessary for the formation decision fusion rule, are presented. The dependencies of the total error probability on the energy parameter taking into account signal-to-noise ratio at the level of local sensors and the channel’s signal-to-noise ratio are given. The gain of the fusion rule basis on the aggregation of soft decisions in the FC when receiving data about soft local decisions, in efficiency compared to hard fusion rule.

Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network

In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.

Algorithm for implementation of the «third decision scheme» of increasing the efficiency of search and synchronization of difficult broadband noise like signals

The construction of telecommunication networks with a guaranteed level of quality of service involves assessing and ensuring the security of both the networks themselves and information flows from unauthorized access and various kinds of interference, in particular at the 1st (physical) level of the network. In this regard, research in the field of creating recurrent code sequences with improved systemic, correlation, secretive, imitation-resistant properties is constantly relevant. In particular, research in the development and application of derivative nonlinear recurrent sequences, as a central element in the developed theory of the «third decision scheme». Goal of the work is to develop an algorithm for increasing the efficiency of search and synchronization of broadband signals in the form of double derivatives of nonlinear recurrent sequences, which directly uses the features of their code structure, the properties of determinism of their auto- and cross-correlation functions. The results of the research and development of the efficiency of the algorithm for accelerated search and synchronization of broadband complex signals in the form of phase-shift keyed derivatives of nonlinear recurrent sequences, which implements the principles of the «third decision schema» The developed algorithm, based on the use of double derivatives of nonlinear recurrent sequences as wideband signals, can be applied in the construction of promising packet radio networks.