JOURNAL OF COMMUNICATION ENGINEERING
Year:2022 | Volume:12 | Issue:45|Papers Count:6

We consider a full-duplex (FD) cellular network, where uplink (UL) and downlink (DL) transmissions are performed at the same time over the same frequency band by using the non-orthogonal multiple-access (NOMA) technique. By leveraging the zero-forcing (ZF) beamforming at the FD multiantenna base station, self-interference is mitigated, and the instantaneous sum rate of the system is maximized. More specifically, we propose two ZFbased beamforming designs at the base station, namely receive ZF (RZF) and transmit ZF (TZF) scheme, which respectively utilize the receive and transmit antennas at the BS to cancel out the SI at the BS. We derive closed-form expression for the outage probability of the NOMA near and far users as a function of different system parameters. Finally, we examine the accuracy of the results by using extensive simulation results. Our numerical results show that for both TZF and RZF scheme, increasing the number of transmit and receive antenna is beneficial to improve the outage performance of the DL near user, while increasing the number of transmit and receive antenna significantly improve the outage performance of the DL far user with RZF scheme.

In this paper, the aim is to cover LTE, band by presenting a new design of multi-input/multi-output radiators an acceptable radiation pattern and gain. The design process in this research is a bipolar radiator with a physical size of 1. 6 × 40 × 40 mm3, when the port number one is excited, the frequency bands are 0. 78-1. 07, 2. 01-2. 67, 3. 74-4. 7. 0. 77-7. 14, 9. 38-10. 10 GHz for the second port. Based on the results, the structure has good isolation between the two ports. By applying a spiral slit in the span of one and one inverted slit and creating a diagonal state in the corners of the element in the second span of circular polarization in the bands of 0. 59-1. 01, GHz in the first port in the left direction and 0. 58-1. 45 GHz is obtained in the second port in a right-handed. This scheme with its charaterestics is good candidate for mobile applications & IoT.

In this paper, for the classification of images, the observed cooperative classification method is proposed with the aim of reducing the semantic vacuum. Because most classification methods are sensitive to cluster centers at initialization, the algorithm converges optimally locally if the quantification is not done correctly. It is also very difficult to combine the results of the classification due to the fact that the labels of the work centers are not clear. Supervised semi-classification is used to solve these problems. To achieve the highest performance, the system classification results are combined with different color space and similarity criteria with multiple features as a semi-supervised cooperative. When the number of features is effective, the relevant feedback is used for its semi-regulatory classification. One of the most important parts of an image retrieval system and a classification algorithm is to determine the appropriate similarity between images. In this study, two methods of classification of cases have been used, which include k-NN and PNN classification, which according to the results in all proposed methods, a better response from k-NN classification than PNN has been observed. The proposed algorithm is very suitable for classifying large image databases due to the reduction of time complexity. Also, in image retrieval in different color spaces and using different similarity criteria, different classifications are obtained. Better results can be achieved if the classification results are combined. Recognition rate on the image data used in this study, on which the pyramidal histogram algorithm of oriented gradients is applied, has the highest rate compared to other proposed methods and is 98. 52%. Experiments on the Corel image database show that the combined method increases the classification accuracy by an average of about 20% in the combined method.

The microgrid inertia as a result of tiny structure and barely tolerance variations, is fairly low. Thus, the maintenance of voltage stability and frequency specifically in islanded mode is extremely demanding. Even if these products have efficient control system, they can’ t retain microgrid stability due to the low speed of response in primary sources of energy and communication delays of the links between outer unit and control system in distributed generation. Introducing a structure of fuzzy control arranged with neural network to balance between generation part and consumption part in microgrid is the main purpose of this paper. Using the fuzzy logic, this controller enables flexible operation of microgrids in both network and islanded modes. In the proposed control system, a trainable neural network in different operating conditions is responsible for fine tuning of the fuzzy logic system. Because of the sensitivity of the loads in the microgrid the proposed structure is designed to interact with the storage source in order to increase the response speed to the imbalance between production and consumption. This might prevent excessive voltage and frequency deviation, especially in the severe situations. With this controller, fluctuations in the production of renewable resources quickly compensated without a negative impact on resilience and instability of the microgrid, especially while disconnecting from the main network.

In this paper, we present an electro-optical modulator based on graphene absorption. In this structure, graphene is placed on aluminum oxide, the structure of which is repeated alternately. This alternating structure of graphene on aluminum oxide is located inside a semi-cylindrical cavity in a 1-dimensional photonic crystal nanobeam cavity. Unlike previous articles, which consisted of cylinder holes and different areas in the nanobeam cavity are formed by changing the radius of the holes, in this structure, semicylinder holes have been used and, different areas are formed by rotating these semi-cylinders. This type of crystal photonics has a high-quality factor. Also, this type of modulator requires little space after construction, In this paper, the three-dimensional finite-difference time-domain method is used for analysis. In this modulator, it is observed that by changing the bias voltage, the amount of absorption peak and also its resonance wavelength are changed. As a result of these changes, we achieve a modulation depth of about 7dB.

In this paper, a new method for robust control of robotic arm position using particle swarm optimization is proposed. The whole robotic system, including the robot arm and motors, is considered in the control problem. The main purpose of this paper is to obtain an optimal estimate of the parameters of the control law in order to achieve the minimum tracking error that congestion optimization is used. Also, the design of the control law is based on the nominal model. The real model uses intelligent systems. Optimal robust control is confirmed by convergence analysis. The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values of the parameters in the torque controllers have not converged to their true values due to the large modeless dynamics, while they have converged to their true values in the voltage control because it has only parametric uncertainty. The torque control law also requires position vector, velocity vector, and acceleration vector feedback. These feedbacks cannot be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedbacks can be easily accessed.