An executable meta-model of a hierarchical and distributed architecture management for cognitive radio equipments

Abstract

A cognitive radio (CR) equipment is a radio device that supports the smart facilities offered by future cognitive networks. Even if several categories of equipments exist (terminal, base station, smart PDA, etc.), with their different processing capabilities (and associated cost or power consumption), this means that apart from the usual radio signal processing elements, these equipments have to integrate also a set of new capabilities for the CR support; this implies not only radio adaptation and sensing capabilities. We assert that it is necessary to add some management facilities for that, and we propose here an architecture management to be inserted inside CR equipments named thereafter Hierarchical and Distributed Cognitive Architecture Management (HDCRAM). This approach is based upon a Hierarchical and Distributed Reconfiguration Management (HDReM), which is derived from our previous research on software-defined radio. The HDCRAM extends the HDReM towards CR while adding new management features, in order to support sensing and decision-making facilities. It consists in the combination of one Cognitive Radio Management Unit (CRMU) with each reconfiguration management unit distributed in the equipment. Each of these CRMU is in charge of the capture, the interpretation, and the decision making according to its own goals. In this cognitive radio context, the term “decision” refers to the adaptation of the radio parameters to the equipment environment. This paper details the management functionality and structure of the HDCRAM. Moreover, this architecture has also been modeled with a meta-programming language based on UML. The first goal is to propose a comprehensive specification of the CR management of future CR equipments. Beyond this objective, we have also derived a simulator from the obtained meta-model, which gives the opportunity to specify CR needs and play a wide variety of scenarios in order to validate the CR design. The example of a Blind Standard Recognition CR scenario illustrates the relevance of this approach.