فهرست مطالب

Information and Communication Technology Research - Volume:11 Issue: 2, Spring 2019

International Journal Information and Communication Technology Research
Volume:11 Issue: 2, Spring 2019

  • تاریخ انتشار: 1399/04/30
  • تعداد عناوین: 7
|
  • Mehdi Shirichian, Gholamreza Moradi*, Reza Sarraf Shirazi Pages 1-6

    The demand for mobile communication systems with high data rate cusses to grow the attention to the millimeter-wave frequency in order to increase the channel capacity compared to current 4G cellular networks. So, the fifth generation (5G) mobile communications is presented which is to work in mm-wave frequency band to improve data rate, capacity and latency simultaneously. One of the challenges of 5G application is the base station and mobile antennas which should be robust against fading. Therefore, in this paper, a two port substrate integrated waveguide (SIW) slot base station antenna for 5G application at 28 GHz is presented. The realized gain of the two-port antenna with a high isolation (20dB) and circular polarization is 6.35 dBi. The 5-element array of the two-port antenna with the same excitation can increase the gain 8 dB. But it has some nulls. With phase shift for excitation ports, the nulls are canceled.

    Keywords: Fifth generation, millimeter-wave, base station, circular polarization, array antenna
  • Narges Noori*, Mohammad Mehdi Tamaddondar Pages 7-17

    This paper presents a novel three-dimensional (3D) time-variant hybrid channel model for massive multiple input multiple output (M-MIMO) wireless systems. The main aim of the proposed model is to find channel characteristics in a simple and precise manner. To achieve this purpose, this channel model consists of two deterministic and stochastic modes. By using the idea of equivalent planes and ray tracing method, the channel multipath components (MPCs) are calculated in the deterministic mode. In the stochastic mode, those parts of the propagation environment that are too complex to be modeled in the deterministic mode, are modeled based on the cluster concept. Then, the MPCs characteristics are calculated by utilizing appropriate random distributions. The doppler effect is also taken into account due to the probability of existence relative velocities among channel components. This model is validated by comparing simulation results with those of the previously developed channel models. Finally, the channel model is applied to a real scenario to extract some of the important characteristics of the propagation environment.

    Keywords: Channel modeling, doppler effect, clustering, massive MIMO, ray tracing, 5G
  • Azar Hakimi, Mohammadali Mohammadi*, Zahra Mobini Pages 18-28

    We consider a non-orthogonal multiple access (NOMA) cooperative spectrum sharing network, where a multi-antenna secondary transmitter assists transmission of a primary transmitter-receiver pair, and at the same time transmits to a secondary receiver. The secondary transmitter is assumed to be full-duplex and energy-constrained. Therefore, secondary transmitter replenishes its battery storage via energy harvesting from an energy access point located in its vicinity. In order to cancel the self-interference at the secondary transmitter, two zero-forcing (ZF)-based beamforming schemes and one maximum ratio combining/maximum ratio transmission (MRC/MRT) scheme are designed. Then, corresponding outage probability analysis of the primary and secondary networks with proposed beamforming schemes are derived. Outage probability results are used to study the delay-constrained throughput of the system. Our results suggest that by utilizing ZF-based beamforming schemes, significant performance improvement can be achieved compared to the half-duplex counterpart. Moreover, our results indicate that proposed ZF-based schemes achieves a zero-diversity order.

    Keywords: component, Non-orthogonal multiple access (NOMA), cooperative spectrum sharing networks, full-duplex, delay-constrained throughput, zero-forcing beamforming
  • Nima Farajian, Peyman Adibi* Pages 29-37

    Recent researches have determined that regularized auto-encoders can provide a good representation of data which improves the performance of data classification. These type of auto-encoders which are usually over-complete, provide a representation of data that has some degree of sparsity and is robust against variation of data to extract meaningful information and reveal the underlying structure of data by making a change in classic auto- encoders’ structure and/or adding regularizing terms to the objective function. The present study aimed to propose a novel approach to generate sparse, robust, and discriminative features through supervised regularized auto-encoders, in which unlike most existing auto-encoders, the data labels are used during feature extraction to improve discrimination of the representation and also, the sparsity ratio of the representation is completely adaptive and dynamically determined based on data distribution and complexity. Results reveal that this method has better performance in comparison to other regularized auto-encoders regarding data classification.

    Keywords: Supervised Auto-encoder, Feature Learning, Discriminative Representation, Manifold
  • Zeinab Nakhaei, Ali Ahmadi*, Arash Sharifi, Kambiz Badie Pages 38-48

    In this paper, we propose an approach to data fusion to enhance the accuracy of data integration. The proposed approach uses the information in the relationships between entities to find more evidence for the correctness or incorrectness of the values ​​generated by different data sources. We also define some concepts and investigate the different methods for identifying relationships between entities. Then, we consider how to use these relationships to increase the accuracy of the conflict resolution process. Unlike many existing approaches, our proposed approach is at the high level of data abstraction. Using the information there exists at the high levels of data abstraction allows us to provide sufficient evidences where data is incomplete and there is no reliable source for the particular object. The evaluation results show that our proposed approach outperforms existing conflict resolution techniques.

    Keywords: conflict resolution, data fusion, truth discovery, relation assessment
  • Neda Zekrizadeh, Ahmad Khademzadeh*, Mehdi Hosseinzadeh Pages 49-61

    Task scheduling is one of the main and important challenges in the cloud environment. The dynamic nature and changing conditions of the cloud generally leads to problems for the task scheduling. Hence resource management and scheduling are among the important cases to improve throughput of cloud computing. This paper presents an online, a non-preemptive scheduling solution using two learning automata for the task scheduling problem on virtual machines in the cloud environment that is called LABTS. This algorithm consists three phases: in the first one, the priority of tasks sent by a learning automaton is predicted. In the second phase, the existing virtual machines are clustered according to the predictions in the previous phase. Finally, using another learning automaton, tasks are assigned to the virtual machines in the third phase. The simulation results show that the proposed algorithm in the cloud environment reduces the value of two parameters makespan and degree of imbalance.

    Keywords: cloud computing, learning automata, task scheduling, priorities of tasks
  • Samiyeh Khosravi* Pages 62-69

    Despite the huge use of cloud computing, due to its large dimensions and availability for all users, this type of network is weak and vulnerable to malicious attacks. Therefore, as a useful complement to existing security methods, trust management plays a crucial role in discovering suspicious behaviors in the cloud computing network. The critical question is, how can we find ideally and effectively users with suspicious behaviors in these complex environments. In this paper, the Markov chain model has been used to calculate the short-term reliability of users in the cloud network, and the trust management system has been proposed to mitigate the effects of complex environments to calculate the user’s status. Furthermore, a new computational model has been introduced with relevant, practical factors for calculating the long-term trust that reduces the effect of changing environmental parameters in the calculations. The simulation results show that the proposed algorithm, Markov chain trust management can more effectively detect suspicious behaviors of users in the cloud computing network, and in a meaningful way, provide a better rate of delivery of packets compared to their counterparts, and ultimately provide better security in the cloud computing network.
     

    Keywords: Cloud Computing, Network Security, Markov chain