Abstract
For a multiuser cognitive OFDM network, most existing adaptive resource allocation methods focus solely on fixed data-rate requirement (QoS guaranteed) or variable data-rate (no QoS guaranteed) conditions without any fairness consideration. In this paper, we investigate the resource allocation problem in a heterogeneous cognitive network with different QoS requirements. By decomposing the problem as a convex optimization, an optimal time-sharing resource allocation method is proposed to maximize the throughput of cognitive network under both subcarrier interference temperature limits and heterogeneous data-rate requirements. Finally, the performance of our proposed resource allocation algorithm is investigated by numerical results.
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