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SUMMARYCognitive radio makes it possible for an unlicensed user to access a spectrum unoccupied by licensed users. In cognitive radio networks, extra constraints on interference temperature need to be introduced into radio resource allocation. In this paper, the uplink radio resource allocation is investigated for OFDMAbased cognitive radio networks. In consideration of the characteristics of cognitive radio and OFDMA, an improved water-filling power allocation scheme is proposed under the interference temperature constraints for optimal performance. Based on the improved water-filling power allocation, a simple subcarrier allocation algorithm for uplink is proposed. The subcarrier allocation rules are obtained by theoretical deduction. In the uplink subcarrier allocation algorithm, the subcarriers are allocated to the users with the best channel quality initially and then adjusted to improve the system performance. A cursory water-filling level estimation method is used to decrease the complexity of the algorithm. Asymptotic performance analysis gives a lower bound of the stability of the water-filling level estimation. The complexity and performance of the proposed radio resource allocation scheme are investigated by theoretical analysis and numerical results.

In cognitive radio network, the interference of the unlicensed users to the licensed users should be limited under interference temperature constraints. In this paper, the optimal power control scheme of a network is analyzed without interference temperature constraints firstly. Based on this, considering interference temperature constraints, the optimal power control in cognitive radio network is modeled as a concave minimization problem. Some useful properties of the power control optimization problem are exploited. According to these properties, an improved branch and bound algorithm which is more efficient than the general branch and bound algorithm is proposed for optimal power control optimization problem in cognitive radio network.