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The invention of cognitive radio concept is to overcome the spectral scarcity issues of emerging radio systems. In cognitive radio, the secondary users can either opportunistically access the licensed spectrum in the absence of their primary users, or buy/rent the spectrum access rights from the primary users. For the former case, spectrum sensing and resource allocation are highly essential. While spectrum sensing aims to identify the unused spectrum through detecting the presence of primary users’ signal, resource allocation exploits information obtained from spectrum sensing to schedule the unused spectrum among the secondary users. For the latter case, spectrum trading is emphasized. The problems of spectrum sensing, resource allocation and spectrum trading in cognitive radio networks are focused on this thesis. For spectrum sensing, due to its sensing accuracy, the cooperative spectrum sensing rather than stand-alone spectrum sensing is studied. The challenges arising when secondary users have no a priori knowledge of primary users’ bands are discussed. These challenges are to dynamically detect the primary user’s bands and to reduce the overheads required to facilitate fusion center operation. A dynamic band clustering algorithm that uses K-means clustering technique, and a novel entropy-based decision-fusion technique are proposed to address these challenges, respectively. The proposed decision-fusion technique is shown to be comparable to conventional information-fusion techniques while reducing significantly the bandwidth overheads. For resource allocation problem, two non-cooperative games named Interference Minimization game and Capacity Maximization game which reflect the targets of data radios and voice radios are proposed, respectively. From the extensive simulation, it is shown that these proposed games help improve the average SINRs and capacities of all secondary users.

Sensing, allocation and trading of spectral resources in cognitive radio networks