kamal mohamed-pour

The underwater acoustic channel is known as one of the most challenging communication channels due to its physical nature. In this regard, the use of orthogonal frequency division modulation (OFDM) and multiple input-multiple output (MIMO) systems are effective methods to overcome channel effects and increase the capacity of the underwater channel. In this way, the performance of these telecommunication systems and the achievement of the mentioned benefits are significantly dependent on the estimation of coefficients and the acquisition of channel state information. Considering that in most researches, the channel between the transmitter and the receiver is assumed to be thin, while in practical applications this is not the case; In this article, two feed-forward deep neural network (FF-DNN) models, Net_1 and Net_2, have been used to estimate the coefficients of multi-tap communication channels in underwater channels. The process is as follows: first, the least square (LS) estimate of the channel is obtained, and then it is applied as an input to these two neural network models, the model is trained and learned, and the output of the least mean square estimate is Error (MMSE) of expected channel coefficients. The results obtained from the simulation show that the use of these two deep neural network models with different numbers of hidden layers by overcoming the LS estimation based on the comparative criteria of MSE and BER has a good performance compared to the MMSE estimation. and increases the quality of coefficient estimation. For example, based on the BER criterion, the presented models have improved by 3 and 5.5 dB respectively for an error value equal to 2-10.

Due to the high-power consumption and complexity of fully digital baseband precoding, its implementation in massive millimeter-wave multiple-input multiple-output (MIMO) systems is not cost-efficient and practical; for this reason, hybrid precoding has attracted a lot of attention in recent years.  Most hybrid precoding techniques concentrate on the fully-connected structure, although they require lots of phase shifters, which is high energy-consuming. On the contrary, the partially-connected structure has low power consumption, nevertheless, suffers from a severe decrease in spectral efficiency (SE). To enhance SE, this paper proposed a dynamic hybrid precoding structure where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF) chains. To determine the digital precoder and the states of switch, a novel alternating minimization algorithm is proposed, which leverages closed-form solutions at each iteration to efficiently converge to an optimal solution. Furthermore, the phase shifter matrix is optimized through an iterative solution. The simulation results show that in terms of SE, the proposed algorithm with a dynamic structure achieves higher performance than the partial structure. Also, since the proposed structure reduces the number of phase shifters, it can guarantee better energy efficiency (EE) than the fully connected structure.

The use of millimeter wave (mmWave) massive multiple input multiple output (Ma-MIMO) systems makes it possible to meet the essential needs of future generation wireless systems and solve the impending wireless network crisis. The mmWave Ma-MIMO Technique offers higher numbers of antennas and carrier frequencies. Hybrid precoding is considered as a key technique for the practical deployment of mmWave Ma-MIMO systems, since it significantly decreases the implementation costs, energy consumption, and hardware complexity. The large using of mmWave Ma-MIMO technologies in future generation wireless systems, causes imperative develop cost-effective hybrid precoding solutions that match the various application cases of these systems. The fully-connected structure can offer spectral efficiency (SE) close to the fully-digital precoding but, unfortunately with high energy consumption. Furthermore, the sub-connected structure with reduced power consumption, provides poor SE. Therefore, the trade-off between SE and energy efficiency (EE), can be made, and in this paper, we consider an adaptive sub-connected (ASC) hybrid precoding structure, where a switch network is able to provide dynamic connections from phase shifters to radio frequency (RF)chains. The simulation results indicate that in terms of SE, the proposed algorithm with ASC structure obtains higher performance than the sub-connected structure. As a result, since the ASC structure reduces the number of phase shifters, it can offer a better EE compared to the sub-connected structure.

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.

Multi-transmitter multi-receiver passive radar, which locates target in the surveillance area by the reflected signals of the available opportunistic transmitter from the target, is of interest in many applications. In this paper, we investigate different signal processing scenarios in multi-transmitter multi-receiver passive radar. These scenarios include decentralized processing of reference and surveillance signals, decentralized processing of surveillance signals, and centralized processing of surveillance signals that have different advantages, disadvantages, and requirements. A variety of possible measurements including TDOA, GROA, AOA and their combinations are presented under different scenarios. The Cramer Rao lower band (CRLB) is presented for different signal processing scenarios for the error of the target localization. The efficiency of the target localization for different signal processing scenarios and types of measurements has been investigated by the CRLB. As shown in the simulation results, the combined use of the measurements is always better than their single use. Also centralized and decentralized processing of surveillance signals, by arranging the receiver sensors in the far distances, can have better efficiency than close distance arrangements.

Positioning involves a wide range of military and industrial applications. In a positioning based on directional measurement, the transmitter's position is the intersection of the hypothetical orientation lines that send out the sensors to the transmitter. In the absence of noise, the location of these lines will be unique. But in reality, existing noise causes uncertainty in measurements and positioning. So far, various approaches have been introduced for orientation measurement based positioning. Most of these methods are based on numerical algorithms and the traditional maximum likelihood in order to estimate the optimal target position. These methods are equivalent to minimizing the total measurement errors by assuming Gaussian noise. Although TML is accurate, but due to the use of a recursive numerical algorithm, when the noise measurements are large or the geometry of the problem is undesirable, it causes a divergence of algorithm. Because of the use of numerical algorithms, these methods do not have a mathematical closed form for the answer. In addition to these methods, limited efforts are made to solve the problem and extract the closed form answer based on the geometric properties of the problem, most notably the Stansfield method. This algorithm is based on the assumption that the Gaussian noise is small and that the transmitter's approximate distance from the sensors is known. In this paper, attention is paid to the geometric properties of the problem in order to extract the closed form answer. But the assumption of the small Gaussian noise and the approximate distance between the transmitter and the sensors are not required. In most papers, the response of the TML algorithm as a benchmark has been compared in order to determine the precision of the proposed method. In this paper, it is shown in several scenarios that the proposed algorithm has a lower RMSE error than the TML, in addition to providing a mathematical closed form answer.

In this paper, the spatial interference alignment (IA) is investigated in the downlink (DL) of a relay-assisted multi-cell multi-user network. The transmission from the base station (BS) to the mobile station (MS) takes place in two phases with the help of the selected relay station (RS). The closed-form IA algorithms are employed in both the BS to RS and RS to MS links. The performance of this cooperative scheme is analyzed in terms of the sum rate and the sum degrees of freedom (DoF) for the amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes. The simulation results of the sum rate show that the DF scheme significantly outperforms the noncooperative minleakage and max-SINR iterative algorithms over the entire range of SNR, and the AF scheme performs very close to the min-leakage algorithm. Moreover, the DF scheme outperforms the noncooperative closed-form IA algorithm in the low and medium-SNR regimes.

After setteling the LEO satellite in the given circuit, the ground station needs to communicate with it for the received of information and control of its subsystems. Due to the fact that satellite communication channels are influenced by shadowing conditions, if a line of sight (LOS) signal exists between the receiver and the transmitter, then the radio channels conditions are good and the Additive White Gaussian Noise channel is modeled as Rician distribution. However, under poor conditions and in the lack of LOS, channel model is Rayleigh, and due to the radio link conditions the channel will be switched between these two models. In a proper model, the time varying behavior of the station – satellite radio link should be considered. The importance attached to this issue is due to its considerable effect on the selection of modulation type, design of channel access method, and error control. In this view, the present study has sought to momentarily determine the range of variations in error probability. Through calculations and simulation, the Bit Error Rate received during the satellite trajectory has been obtained for any given moment and its range of variations has been determined.

In this paper, we propose a new channel estimation algorithm for Orthogonal Frequency Division Multiplexing (OFDM) systems, which is extended to the Alamouti Coded OFDM case by extracting required equations. At the first stage of this algorithm, the channel response is estimated for pilot sub-carriers and then, the channel response for data sub-carriers is interpolated by Gaussian Radial Basis Function Network as it is an efficient nonlinear interpolator. The post-processing and filtering on the estimated channel taps energy are proposed to improve the estimation performance and reduce the noise effects of primary estimation, which is caused by LS estimation at pilot data. At next step, the accuracy of our channel estimation algorithm is improved by using iterative decision feedback. The decision feedback method is used to update channel parameters in each OFDM symbol. The effectiveness of proposed techniques is demonstrated through the simulation of OFDM and an Alamouti coded OFDM system with two-transmit and two-receive antennas. Finally, the results are analyzed and compared with previous conventional estimation algorithms. Simulation results show that the proposed algorithms achieve efficient performance in many practical situations.