Repository logo

Browsing by Author "Cardenas-Juarez, Marco"

Now showing 1 - 2 of 2
  • Adding Quality in the User Requirements Specification A first approach
    Publication . Guerra-García, César; Caballero, Ismael; Cardenas-Juarez, Marco; Robles, Ramiro
    Users need trusting in data managed by software applications that are part of Information Systems (IS), which supposes that organizations should assuring adequate levels of quality in data that are managed in their IS. Therefore, the fact that an IS can manage data with an adequate level of quality should be a basic requirement for all organizations. In order to reach this basic requirement some aspects and elements related with data quality (DQ) should be taken in account from the earliest stages of development of software applications, i.e. “data quality by design”. Since DQ is considered a multidimensional and largely context-dependent concept, managing all specific requirements is a complex task. The main goal of this paper is to introduce a specific methodology, which is aimed to identifying and eliciting DQ requirements coming from different viewpoints of users. These specific requirements will be used as normal requirements (both functional and non-functional) during the development of IS awareness of data quality.
    2016Conference object Restricted access Show more
  • Performance analysis of superimposed training-based cooperative spectrum sensing
    Publication . Lopez-Lopez, Lizeth; Cardenas-Juarez, Marco; Stevens-Navarro, Enrique; Garcia, Abel; Aguilar-Gonzalez, Rafael; Robles, Ramiro
    Superimposed training (ST) technique can be used at primary users’ transmitters to improve parameter estimation tasks (e.g. channel estimation) at primary users’ receivers. Since ST adds the training sequence to the data sequence the total available bandwidth is used for data transmission. The exploitation of the ST sequence in the context of cognitive radio networks leads to a significant increase in the detection performance of secondary users operating in the very low signal-to-noise ratio region. Hence, a considerably smaller number of samples are required for sensing. In this paper, the performance of STbased spectrum sensing in a cooperative centralized cognitive radio network with soft-decision fusion is studied. Furthermore, a throughput analysis is carried out to quantify the benefits of using ST in the co
    2018Conference object Restricted access Show more