Generalized spectral footprint minimization for OFDMA-based cognitive radio networks

We consider joint subchannel and power allocation for an orthogonal frequency division multiple access (OFDMA)-based cognitive radio network. We formulate the downlink resource allocation problem as a spectral-footprint (bandwidth-power product) minimization problem under interference threshold at primary users, total power and quality of service constraints. The cognitive base station solves this non-convex mixed-integer programming problem iteratively by dividing it into a subchannel allocation master problem and power allocation subproblems. The subchannel assignment problem is solved by applying a modified Hungarian algorithm while the power allocation subproblems are solved by using Lagrangian techniques. Specifically, we propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. The performance of our spectral-footprint minimization technique is compared with the waterfilling power allocation.