In any wireless cellular network, power control is one of the most important dynamic radio resource management (RRM) schemes which increases the capacity and performance of the system. In this paper, we present a modified scheme to Distributed Power control that optimize the transmission power of mobile’s and signal-to-interference-plus-noise ratio (SINR) error. This method, based on minimization of performance criterion, achieves the minimum SINR error and power consumption at the next time instant. The Mixed Kalman/H∞ Filter with covariance intersection has been applied in the proposed scheme to estimate and predict the channel variation and still ensure a good robustness. Finally, the mixed Kalman/H∞ filter based power control method is compared with Kalman filter and H∞ filter based power control results show that our method provides robustness against practical impairments, such as measurement uncertainties and fast channel variations.

High order modulation (HOM) presents a key challenge in increasing spectrum efficiency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The artificial neural network’s (ANN) adaptive equalizer architectures and learning methods are simplified to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive filters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers. © 2018 National Institute of Telecommunications. All rights reserved.

The study of propagation characteristics is a fundamental step in mobile radio engineering; which is intended to achieve maximum performance for a mobile radio system. To do this, the propagation models are essential tools for this study such as the evaluation of the signal strength received by a mobile terminal, the evaluation of coverage radii and deduce the number of cells needed to cover a given area, such as radio planning, which in turn is the step that aims to estimate the necessary equipment and configurations of the radio interface. In this work we adopt the standard K factor model and OKUMURA HATA model to demonstrate a propagation model adapted to the physical environment of the city of Annaba in Algeria using a linear regression algorithm based on the ordinary least squares method. Radio measurements were carried out on the CDMA network of operator Mobilis. The calculation of the square root of the mean square error between the actual data and the radio measurements and the prediction data derived from the model implemented allowing the validation of the results obtained. A comparative study between the value of the RMSE obtained by the new model and those obtained by the models K standard factors and the model of OKUMURA HATA allows us to conclude that the new model is better adapted to our local environment than that of OKUMURA HATA. The new model obtained can help increase the performance of mobile radio systems deployed in our territory. © 2017, School of Electrical Engineering and Informatics. All rights reserved.

Recently, researches in the interventional microrobots have taken the lion’s share in the field of biomedical devices. The aim of biomedical microrobots is to reach inaccessible areas of the human body and deliver drugs in high position. This work presents a new approach to elaborate a new physics-based model for novel self-propelled swimming microrobots. The robot is composed of an ellipsoidal head and hybrid tail that are propelled by a joint polymer metal composite actuator. Green’s function is used to solve the coupled elastic/fluid problems caused by the vibrating hybrid tail in a fluid. This method allowed producing the velocity of microrobot. For the control of the swimming microrobot in hazardous environment, the flatness-fuzzy-based control strategy is developed to eliminate the effect of nonlinear model and to generate the optimal trajectory of flat outputs. The fuzzy technique is aimed to adjust the law control gains in real time for improving the precision of the proposed microrobot in tracking the desired trajectory in fluid. The multi-objectives genetic algorithm is employed to optimize both the reference trajectory and the design parameters in order to enhance the time response and to minimize the dynamic tracking error of the trajectory. To achieve this, a numerical model based on accurate solutions of Navier–Stokes equations is developed. The results of the simulation show that the proposed design with ellipsoidal head gives better performance in comparison with that achieved by the conventional structure.

Recently, the desire for a self-powered micro and nanodevices has attracted a great interest of using sustainable energy sources. Further, the ultimate goal of nanogenerator is to harvest energy from the ambient environment in which a self powered device based on these generators is needed. With the development of nanogenerator-based circuits design and optimization, the building of new device simulator is necessary for the study and the synthesis of electromecanical parameters of this type of models. In the present article, both numerical modeling and optimization of piezoelectric nanogenerator based on zinc oxide have been carried out. They aim to improve the electromecanical performances, robustness, and synthesis process for nanogenerator. The proposed model has been developed for a systematic study of the nanowire morphology parameters in stretching mode. In addition, heuristic optimization technique, namely, particle swarm optimization has been implemented for an analytic modeling and an optimization of nanogenerator-based process in stretching mode. Moreover, the obtained results have been tested and compared with conventional model where a good agreement has been obtained for excitation mode. The developed nanogenerator model can be generalized, extended and integrated into simulators devices to study nanogenerator-based circuits.

This article deals with the study of a new swimming microrobot behavior using an analytical investigation. The analyzed microrobot is associated by a spherical head and hybrid tail. The principle of modeling is based on solving of the coupled elastic/fluidic problems between the hybrid tail’s deflections and the running environment. In spite of the resulting nonlinear model can be exploited to enhance both the sailing ability and also can be controlled in viscous environment using nonlinear control investigations. The applications of the micro-robot have required the precision of control for targeting the running area in terms of response time and tracking error. Due to these limitations, the Flatness-ANFIS based control is used to ensure a good control behavior in hazardous environment. Our control investigation is coupled the differential flatness and adaptive neuro-fuzzy inference techniques, in which the flatness is used to planning the optimal trajectory and eliminate the nonlinearity effects of the resulting model. In other hand, the neuro-fuzzy inference technique is used to build the law of control technique and minimize the dynamic error of tracking trajectory. In particular, we deduct from a non linear model to an optimal model of the design parameter’s using Multi-Objective genetic algorithms (MOGAs). In addition, Computational fluid dynamics modeling of the microrobot is also carried out to study the produced thrust and velocity of the microrobot displacement taking into account the fluid parameters. Our analytical results have been validated by the recorded good agreement between the numerical and analytical results.