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college of information engineering
al nahrain university
Yasmine M. Tabra and Bayan Sabbar
Indonesian Journal of Electrical Engineering and Computer Science, ISSN: 25024752, eISSN: 25024760, Pages: 715-723, Published: 2019 Institute of Advanced Engineering and Science
<p>With the high speed of communication in LTE-5G, fast beamforming techniques need to be adopted. The training time required to form and steer the main lobes toward 5G multiple users must be short. Least-Mean-Square (LMS) training time is not suitable to work with in LTE-5G, but it has a good performance in forming multiple beams to large number of users and producing nulls in the interference direction. In this paper, an optimized hybrid MVDR-LMS beamforming algorithm is proposed to reduce the time required to estimate the antenna’s weights. This optimization is made by the benefit of previously set weights calculated using MVDR algorithms. The performance of the proposed hybrid MVDR-LMS algorithm tested using MATLAB 2016a.</p>
Bayan Mahdi Sabar and Yasmine M. Tabra
Telkomnika (Telecommunication Computing Electronics and Control), ISSN: 16936930, eISSN: 23029293, Pages: 946-954, Published: June 2018 Universitas Ahmad Dahlan
Providing simple and low complexity algorithms for estimating the direction of arrival in large systems using Massive MIMO is considered an important issue. In this paper a method with reduced complexity was proposed to estimate the direction of arrival in FD- MMIMO. The Separated Steering Matrix (SSM) algorithm uses two separated equations for estimating elevation and azimuth angles of Multi-users. This method reduces the complexity of calculating the covariance matrix by decreasing the size of this matrix. This technique is tested using 2D-MUSIC algorithm. Since the mobility of devices affects the accuracy of direction estimation, thus the effect of the initial phase of transmitted signal from mobile device is tested.
Mumtaz M. Al Mukhtar and Yasmine M. Tabra
International Journal of Internet Technology and Secured Transactions, ISSN: 1748569X, eISSN: 17485703, Pages: 42-54, Published: 2012 Inderscience Publishers
The volume of mass unsolicited e-mail, often known as spam, has recently increased enormously and has become a serious threat to not only internet but also to society. It is challenging to develop spam filters that can effectively eliminate the increasing volume of unwanted e-mails automatically. The present work presents a combination of support vector machine classifier for non-linear data (using an eligible kernel function) with appropriate data pre-processing as a spam filter. Data pre-processing is a vital part of text classification where the objective is to generate feature vectors usable by SVM kernels. The pre-processing steps include HTML removal, HTML replacement, de-obfuscation and stop-word-remover. The results obtained using the pre-processing level showed an improvement in the classification level. The estimated training and classification time for different document sizes indicate that the adopted method is practical and computationally efficient. Experimental results show that the approach can enhance the filtering performance effectively.