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Beamforming methods based on minimum variance have poor performance in case there is an error in estimating the covariance matrix of noise and interference. One of the error factors in estimating the covariance matrix is the presence of desirable signal components in estimated noise and interference vectors, which reduces the output SINR level of the beamformer. In this paper, in order to make the beamformer algorithm robust to the incorrect estimation of noise and interference covariance matrix, a covariance matrix reconstruction method using the orthogonal steer vectors obtained from the Gram Schmidt algorithm along with a diagonal loading is applied. Simulation results show the superiority of the proposed method in the improvement of beam pattern, angle estimation of interferences, and output SINR level, compared to the counterparts. [ABSTRACT FROM AUTHOR]

Tourism contains dynamic perspectives and the opportunity for the employment and development of rural entrepreneurship that can be highlighted by recognizing mixed marketing factors to recognize and absorb the destination. The purpose of this study is to investigate the impact of tourism marketing (Place and People) on rural entrepreneurship in Rijab, Kermanshah province. This is a survey of experts in this field (travel and rural entrepreneurship) that can be used to travel with your family. This concept provides you with theoretical predictions, research, research and investigation using consulting and decisionmaking services to allow you to conduct the interview and use financial resources and consulting in this area. After the validation and reliability of the questionnaire, extreme data is gathered purposefully and considering three factors including being responsible, being doable, and being entrepreneurship in tourism industry. SPSS software and Smart PLS were applied to analyze the descriptive and inferential data. Models are fitted by referring to mean root of standardized residual squared root (SRMR), Model Fitting Penalty (EMFT), Normal Fitting Store (NFI) and your root squared covariance matrix (RMS_Theta). The results of data analysis show that location dimension with 51% and people dimension with 52% were effective on rural entrepreneurship development in Rijab region of Kermanshah province. [ABSTRACT FROM AUTHOR]