Cellular OFDMA Cognitive Radio Networks: Generalized Spectral Footprint Minimization

Abstract

We consider the problem of joint subchannel and power allocation for an orthogonal frequency-division multiple-access (OFDMA)-based cognitive radio network (CRN). We formulate the downlink resource-allocation problem as a generalized spectral footprint (SF) (bandwidth–power product) minimization problem under the interference threshold at the primary users (PUs), as well as the total power and quality-of-service constraints. A cognitive base station (BS) solves this nonconvex mixed-integer programming problem iteratively by dividing it into a subchannel-allocation master problem and a power-allocation subproblem. The subchannel assignment problem for secondary users (SUs) is solved by applying a modified Hungarian algorithm, whereas the power-allocation subproblem is solved by using a Lagrangian technique. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation that exploits the local information in the cost matrix. To apply the modified Hungarian algorithm, we require knowledge of the exact number of subchannel requirements of each user in every iteration. Hence, we develop an algorithm to update the number of subchannels required by each user in each iteration based on the SF difference of each user. An asymptotic analysis is carried out for the single SU case, and a closed-form expression is derived for the optimal number of subchannels that minimizes the SF. The performance of our generalized SF minimization technique is compared with the water-filling power-allocation scheme and a scheme based on brute-force search. In addition, several applications of the proposed algorithms are outlined.