MAC protocol identification using support vector machines for cognitive radio networks

Abstract

Cognitive radio is regarded as a potential solution to address the spectrum scarcity issue in wireless communication. In CR, an unlicensed network user (secondary user) is enabled to dynamically/adaptively access the frequency channels considering the current state of the external radio environment. In this article, we investigate the medium access control protocol identification for applications in cognitive MAC. MAC protocol identification enables CR users to sense and identify the MAC protocol types of any existing transmissions (primary or secondary users). The identification results will be used by CR users to adaptively change their transmission parameters in order to improve spectrum utilization, as well as to minimize potential interference to primary and other secondary users. MAC protocol identification also facilitates the implementation of communications among heterogeneous CR networks. In this article, we consider four MAC protocols, including TDMA, CSMA/CA, pure ALOHA, and slotted ALOHA, and propose a MAC identification method based on machine learning techniques. Computer simulations are performed to evaluate the MAC identification performance.