مجله مهندسی برق و الکترونیک ایران
سال هفدهم شماره 1 (بهار 1399)

In this paper, a new method for enhancing the physical layer security of an extended Alamouti space-time coding systems is presented. In this method, the transmitted signals from each antenna are designed in such way to be related to the corresponding channel of the intended receiver, assuming that the transmitter has the knowledge of that channel. Furthermore, the transmitter uses artificial noise and more than two antennas to obtain an additional security. Theoretical mathematical formulas are derived for one-branch, two-branch, and artificial noise aided multi-branch scenarios. Simulation results show the ability of proposed method in enhancing physical layer security compared with other existing methods.

Rice classification and detection of its quality as a main field in the modern agriculture is attracted many researchers in recent years. This problem is a major issue in the scientific and commercial fields associated with modern agriculture. Different processing techniques in recent years are applied to recognize various types of agricultural products. There are also several color-based and texture-based features to achieve the desired results in this classification procedure. In this paper, the problem of rice categorization and quality detection is considered using sparse non-negative matrix factorization algorithm. This technique includes non-negative matrix factorization method with sparsity constraint to achieve dictionaries that represent the structural content of rice variety. Also, these dictionaries are corrected in such a way to yield the dictionaries with least coherence values to each other. The results of the proposed classifier based on the learned models are compared with the results obtained from the neural network and support vector machine classifiers. Simulation results show that the proposed method based on the combinational features is able to identify the type of rice grain and determine its quality with high accuracy rate.

Local smoothness and nonlocal self-similarity of natural images are two main priors in single image super resolution (SISR) problem. Although local sparsity is efficiently utilized to describe the local smoothness, but ignoring the correlation between the sparse representation coefficients of similar patches can lead to inaccurate sparse coding coefficients. In this paper, we propose the method that enforce the local smoothness and nonlocal self-similarity by sparse representation in a unified framework, called adaptive group-based sparse domain selection (A-GSDS). Nonlocal patches with similar structures are exploited and stacked in the form of matrix as the basic unit of sparse representation called group. These groups are converted into a column vector, each column selects the best fitted PCA sub dictionary which is learned from the training data. The sparse coding process for each column in the domain of group leads to find sparse vectors which can be easily estimated by the selected orthogonal sub dictionaries. To further improve the performance of the group-based sparse representation, we use nonlocal means regularization term.  Extensive experimental results validate the effectiveness of the proposed method comparing with the state-of- the-art algorithms.

In this paper, a supervised hyperspectral unmixing solution that integrate the spatial information in the abundance estimation procedure is presented. The proposed method is applied on a nonlinear model based on Polynomial Postnonlinear Mixing Model (PPNMM) where characterizes each pixel reflections composed of nonlinear function of pure spectral signatures corrupted by noise. The image is iteratively classified to classes where contains similar spectral reflectance so share the same abundance vector. Then the abundance vector is estimated for all pixels belong to each class. To make classification, the spatial correlation between pixels belonging to each class is modelled by Markov Random Field (MRF). A Bayesian framework is proposed to estimate the classes and corresponding abundance vectors alternatively. Due to complexity of derived likelihood function, a Markov Chain Monte-Carlo (MCMC) algorithm is used to estimate the abundance vector based on generated samples. The result of implementation on simulated data shows around 20% prominence of proposed approach in comparison to nonlinear unmixing and MRF-based linear unmixing algorithms in the sense of estimation and reconstruction error.

Novel Ultrasonic imaging for weld inspection are based on employing ultrasonic phase arrays. The Total Focusing Method (TFM) is of recent imaging methods with utilizes the maximum available information achieved by array elements and cause a better defect detection compared with conventional methods. In contrast to its ability, TFM produces a considerable background noise which leads to a low resolution and accuracy of defect detection. In this paper an improved TFM method is presented that weights the received element signals using minimum variance theory.  In order to reduce the computational complexity and improve the detection accuracy, the weight computation is performed employing an iterative method such that in each iteration, the inverse of the correlation matrix resulted from the received signals is approximated in a recursive manner and then, is employed in the minimum variance based weight computation. Simulation results and experiments confirm the superiority of the proposed method.Based on the simulation results, the proposed method reduces the average background noise about 16 dB compared to TFM and also improves the brightness of test block defects of ultrasonic image about 8 dB which means a better detection accuracy. Experiments verify the superiority of the proposed method, quantitatively and qualitatively. In addition, the runtime required for the proposed weighting operations is typically 10% of that for Gauss-Jordan removal technique applied to the weighted TFM imaging method.

In this paper, uplink of single-cell massive multiple-input multiple-output (mMIMO) system with receiver hardware impairment is considered. Users have limited energy budget in each coherence interval which should be allocated optimally for uplink pilot and data symbol transmission. Also, we assume that for both channel estimation and data transmission phases receiver suffers from hardware impairments. Hence, using the received pilots the estimates of the channels are obtained at the receiver then by use of these estimates and maximum ratio combining (MRC) technique achievable rate of the users are derived for different values of the transmitted pilot and data powers. Next, a fairness optimization problem is proposed so as to maximize the minimum rate of the users by considering energy budget constraint in a coherence interval. The original optimization problem is a nonconvex one. However, we reformulate it such that it can be solved efficiently using geometric programming. Finally, numerical results are provided which signify the considerable improvements by jointly allocating pilot and data powers in comparison with simple equal power allocation or only optimizing the data power.

In this paper, power and sub-carrier allocation in a MIMO-OFDM based multicast system are jointly proposed to increase the system capacity. The issue of fair allocation of the resources between the multicast groups is also discussed. To achieve these goals, a sub-optimal allocation algorithm is proposed. The proposed algorithm, in addition to have a low computational complexity, allocates a certain amount of resources to all users in the multicast group. In addition, an optimal combination of the genetic and particle swarm optimization algorithms is proposed for joint sub-carrier and power allocation between the multicast groups. The simulation results show that the proposed methods increase the total system capacity compared to the previous ones.

With increasing demand for services, in the fifth-generation (5G), some locally data transmission methods based on efficient energy are proposed. One of these methods is device-to-device communication (D2D), which has been proposed as a promising method to improve the capacity and reduce the power consumption in the wireless networks. D2D communications, to nearby users, allows the data to be transmitted directly, without using the base station. On the other hand, the auxiliary relay can be used in D2D communications. Selecting the proper relay is one of the important to maximize the network throughput. This choice can be based on physical conditions, the amount of energy and social relations. In this paper, a new algorithm is expressed for selecting the optimal relay based on these three parameters, and the effect of each one has been studied. To find the optimal power allocation for relay and D2D users, an optimization problem with the goal of maximizing the energy efficiency is regulated and solved. Simulation results show that the relay selection significantly increases the energy efficiency of the network based on the combination of three parameters social relations, energy harvesting, and data rate.

Wireless sensor networks consisting of tens or hundreds of sensors that are based on intended use in a distributed environment and their main purpose is to record information. In numerous applications, sharing information in short period of time is gravely important. Also, sharning information would spend most of system’s energy and considering the limitaions of energy resource in system, controlling the energy usage in network is a challenge. So, the most important problems in wireless sensor network are energy consumption and latency on the network. To optimize energy consumption and transmission delay, separately and hand-to-hand, meta-heuristic algorithms in wireless sensor network can be used. Based on results, hand-to-hand optimization of these two parameters can increase longevity and performance of the network. The results show that by repeating optimization algorithm, suggested Metaheuristic algorithm’s results are more similar to results of Shortest path algorithm, in comare to results of Particle Swarm Optimization (PSO) algorithm

In this paper, a bidirectional controlled quantum teleportation protocol is presented in which the users can simultaneously transmit a three-qubit pure GHZ state  to each other under monitoring a controller through a 10-qubit channel. This protocol is based on CNOT, single-qubit measurements in the  and  bases and an appropriate unitary matrix to reconstruct the transmitted qubits. The proposed approach is the first one for a bidirectional controlled quantum teleportation protocol for more than two qubits.

In this paper, beamforming and consumed power minimization in base station is studied for an underlay device-to-device communication within a cellular network. It is assumed that the channel state information are not available at the base station, and only estimations of their covariance are available. The estimation errors are considered in two models. In the first model, the errors are upper bounded in their Frobenius norms and the goal is to find the beamforming in the base station in order to minimize the consumed power in BS under the constraint that the signal to interference plus noise ratio for users are guaranteed to be above a certain predefined threshold. In the second model, a probabilistic model is considered for the uncertainty of channel covariance matrices and the BS consumed power is minimized while the non- outage probability of users are guaranteed to be above a certain predefined threshold. Although our approaches initially lead to non-convex optimization problems, they can be reformulated in convex forms by using the semidefinite relaxation technique to find  lower bounds for them. Simulation results show that our proposed methods outperform Hybrid MRT-ZF, ZFBF and MRT beamforming schemes.

Femtocell technology was emerged to solve the problem of growing demands for high data rates in cellular communications networks. It improves the indoor coverage and provides more spatial reuse and higher-quality links for users in a cost effective manner and at the same time, enhance the network capacity. Despite all potential benefits of femtocells, there are some important issues (e.g., the interference and the outage probability) that should be addressed before their deployment. The main goal of this paper is to improve the user performance in femtocell networks using the ultra-wideband (UWB) technology as an effective way to reduce the interference. In this technology, transmitters use a low power signals in a very wide band and work without any harsh effects on adjacent communication systems. (The systems based on this technology create the least interference on narrow band systems which are located in their neighbor.)  Considering femtocells enabled with the UWB technology, femto users can operate without any significant interference on macro users. To study the efficiency of this network, we assume that macrocell and femtocell users are distributed according to the Poisson point process in the network. We calculate the outage probability of macro users which receive the UWB interference signal from the neighbor femto access points along with the outage probability of femto users which receive the narrow band signal of macro base station as the interference. Simulation results indicate that applying the UWB technology in femtocell networks effectively improve the outage probability of users.

The idea of using free space optical communications in seas has developed extensively to investigate the natural water for scientific and financial interests. Underwater wireless optical communications (UWOC) is a new technology using optical carriers to transmit information in water. Usage of Optical carriers increases the rate and bandwidth of data transmission extremely compared to the previously method of acoustic communications. However, it is limited to short distances due to high water absorption in optical frequencies. In addition, other factors including multiple scattering and turbulence degrade the performance of UWOC system. The unique features of water cause the free space optical communications channel models not to be suitable for UWOC channels. To have acceptable data rate and bit error rate in UWOC, it is essential to investigate thoroughly the effect of key factors on light propagation in channel models. To achieve this goal, this paper is investigating the existing UWOC channel models in literature. In these models, the effects of absorption, scattering and turbulence have been considered. Studying the channel modelling techniques, is a great help for proposing a comprehensive and extensive channel model.

Passive source localization is the aim of many applications in the commercial and military fields. In this paper, we propose a new localization approach that using joint time difference of arrival (TDOA), gain ratio of arrival (GROA), and angle of arrival (AOA) of the received signal in the number of the sensors. It will be shown that the performance of localization, using joint measurements is out-performed compared with each of them separately by presented Cramer Rao Lower Band (CRLB). It is shown that the estimator performance is equal to this presented CRLB.