A novel PAPR reduction scheme based on selective mapping and a random-like coding with no explicit side information in OFDM

Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high data rate and reliable communication over fading channels. The main implementation drawback of this system is the possibility of high Peak to Average Power Ratio (PAPR). In this paper, we develop a novel Selective Mapping (SLM) PAPR reduction technique. In the novel proposed scheme, the alternative symbol sequences are generated by module 2 additions of data with the rows of cyclic Hadamard matrix with the same size, inserting the selected row's number to avoid transmitting any side information and specially using a random-like Irregular Repeat Accumulate (IRA) encoder for both PAPR and Bit Error Rate (BER) better performance. Keywords: IRA Codes, OFDM, PAPR, SLM method

Developing a Framework for Exploring Factors Affecting on Trust in M-Commerce using Analytic Hierarchy Process

Mobile Commerce is a developing and maturing area of electronic Commerce, where customers andvendors can interact via the service providers through a wireless network and mobile devices forinformation retrieval and transaction processing. In mobile transactions trust is an essential constituent inmobile commerce transactions .This study aims to clarify the factors that affect on trust in mobilecommerce, and then evaluate and asses these factors by AHP method. This paper provides a theory basedframework that helps to customers to make a right decision while they would like to shop via mobilefacilities in mobile browsers. For this purpose the contribution of different scientific approaches isexamined. By combining these approaches a framework for the classification is derived for trust model.Keywords: AHP method, Mobile Commerce, Trust, Security

Application of K-nearest neighbour predictor for classifying trust of B2C customers

K-nearest neighbor (k-NN) classification is one of the most fundamental classification methods and should be one of the first choices for a classification study when there is little or no prior knowledge about the distribution of the data. In addition, nearest neighbor analysis is a method for classifying cases based on their similarity to other cases. In this paper using k-NN method some factors that affect on customer trust in online transactions, were classified. Raw data gathered from customers when they were buying as customer in B2C websites. One questionnaire was developed and data was gathered from online customers. After organizing data, k-NN method was applied and desired results were obtained. Results showed that in which positions customer can trust to B2C websites and which factors are more significant. Accordingly, in this paper k-NN enable us to predict role of factors on trust level in five levels