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1 - Blind Number of Active Users Estimation and Synchronization in CDMA Systems with Unequal Power’s Signal on Flat Fading Channel
S. Ghavami V. Tabatabvakili
20.1001.1.16823745.1389.8.3.1.5

Blind estimation of number of active users and synchronization of those have important role in identification of system’s parameters, for the spectrum surveillance system design of multi-user direct sequence spread spectrum systems. For the number of active user estimat More

Blind estimation of number of active users and synchronization of those have important role in identification of system’s parameters, for the spectrum surveillance system design of multi-user direct sequence spread spectrum systems. For the number of active user estimation, signal’s eigenvalues of the received signal covariance matrix is distinguished from those of noise using an adaptive threshold. Analytical results and computer simulations show that signal of users with limited received power difference (less than 1.5 dB) can be synchronized using maximizing the Frobenious norm of the received signal covariance matrix. Moreover, this method is robust against of the carrier frequency offset which is due to Doppler shift in the carrier frequency of different users. By increasing difference among received powers of processed signals, performance of this method will be degraded. Hence, an iterative algorithm for interference cancellation of users with higher received power is proposed, which can be synchronize and estimate the number of active users with lower received power. The proposed method for number of active user estimation reduces computational complexity in comparison with the traditional methods for number of active user estimation, such as MDL and AIC, while keep the accuracy of estimation in low SNR regime. The performance of blind synchronization method in the flat fading channel is degraded; hence, a multi-antenna receiver is proposed to improve the probability of correct synchronization. Manuscript profile
Open Access Article
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2 - A Novel Method Based on Non-Negative Matrix Factorization for Dimensions Reduction
Mehdi Hosseinzadeh Aghdam مرتضی آنالویی Jafar Tanha
20.1001.1.16823745.1401.20.2.4.0

Machine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative mat More

Machine learning has been widely used over the past decades due to its wide range of applications. In most machine learning applications such as clustering and classification, data dimensions are large and the use of data reduction methods is essential. Non-negative matrix factorization reduces data dimensions by extracting latent features from large dimensional data. Non-negative matrix factorization only considers how to model each feature vector in the decomposed matrices and ignores the relationships between feature vectors. The relationships between feature vectors provide better factorization for machine learning applications. In this paper, a new method based on non-negative matrix factorization is proposed to reduce the dimensions of the data, which sets constraints on each feature vector pair using distance-based criteria. The proposed method uses the Frobenius norm as a cost function to create update rules. The results of experiments on the data sets show that the proposed multiplicative update rules converge rapidly and give better results than other algorithms. Manuscript profile

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List of Articles نرم فروبنیوس