INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS Int. J. Commun. Syst. (2012) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/dac.2350 Opportunistic channel allocation decision making in cognitive radio communications Hazem Shatila, Mohamed Khedr * ,† and Jeffrey H. Reed Bradley Department of Electrical and Computer Engineering, Wireless@VirginiaTech. Virginia Polytechnic Institute and State University Virginia, VA, USA 24061 SUMMARY The global spread of wireless devices with mobile Internet access and the increasing demand of multimedia- based applications are fueling the need for wireless broadband networks. IEEE 802.16 and 802.20 are standards for a broadband wireless access with promising cognitive radio features to support mobile Internet access. However, because of the fast changing radio environment and the demand for dynamic spectrum allocation mechanisms, these standards must continuously readjust different radio parameters. The cognitive radio makes decisions based on its built-in inference engine, which also in time can adapt itself to different situations through the process of learning from experience. In this paper we present an automated opportunistic decision making and learning process for cognitive radio based on uncertainty reasoning algorithms. This novel approach is well suited in fast changing wireless environments with vague, incomplete, and heterogeneous information. Theory and simulations prove that decision making and learning of the cognitive radio based on the proposed approach cope with the changes in the radio environment. In this work we use fuzzy logic for the learning and decision making of the cognitive radio. Simulation also show that our approach provides accurate and precise decisions on allocating spectrum to mobile Internet users even in fast varying radio conditions. Copyright © 2012 John Wiley & Sons, Ltd. Received 16 May 2011; Revised 13 January 2012; Accepted 6 March 2012 KEY WORDS: wireless broadband; cognitive radio; fuzzy logic; fuzzy C-mean clustering; dynamic spectrum allocation; opportunistic decision making 1. INTRODUCTION The need for high speed and high quality wireless service has motivated the development of IEEE 802.16 and the IEEE 802.20 [1, 2]. These standards promise high speed communications and guaranteed quality of service and provide broadband wireless connectivity and mobile Internet access to fixed and mobile users. They are based on adaptive modulation with OFDM and have impressive capabilities especially in non-line-of-sight environments [2]. These technologies can potentially be used to provide backhaul in many networks such as IEEE 802.11 hotspots, cellular networks, and wireless local area networks. However, radio resource management is one of the main challenges for providing good quality of service and reliable connectivity to mobile Internet users. As a consequence, cognitive radio (CR) has been proposed and used to improve spectrum and other radio resources utilization. In other words, cognitive radio can detect the presence of unoccupied spectrum gaps and modify the transmission parameters and operating frequencies to efficiently use these empty bands. Cognitive radio was first defined by Mitola et al. [3] as ‘A radio that employs model based reasoning to achieve a specified level of competence in radio-related domains.’ and was defined in [4] to have six important key functions: *Correspondence to: Mohamed Khedr, Arab Academy for Science and Technology. † E-mail: khedr@vt.edu Copyright © 2012 John Wiley & Sons, Ltd.