In this paper, we study the resource allocation issues related to OFDM-based multiuser cognitive radio cellular networks, where both of primary network and secondary network are investigated to make an all-round evaluation of the network performance, including the Quality of Experience (QoE) of the primary users (PUs) and the minimal rate requirement of the secondary users (SUs). Since the general resource allocation optimization task we formulate leads to challenging mixed integer nonlinear programming problems, we first propose a heuristic subchannel allocation method to remove the awkward integer constraints. Then an efficient distribution algorithm, which yields nearly linear computational complexity, is developed to work out the optimal power allocation. Simulation results indicate that our proposed resource allocation scheme works better under different performance metrics as compared to others. Moreover, our proposed distribution algorithms converge stably and quickly.

In this paper, we propose a clustering-based resource allocation (RA) scheme for the multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio network, where we aim to maximize the sum capacity of the secondary users (SUs) subject to practical constraints in wireless environment. Our general RA optimization task leads to a challenging mixed integer programming problem that is computationally intractable. We first introduce a simple and efficient clustering method to divide all the SUs into multiple groups based on their mutual interference degrees, where the SUs in different groups can share the same OFDM subchannels to improve spectrum utilization efficiency, while the SUs with heavy mutual interference cluster together in the same group and employ different subchannels to alleviate their mutual interference. Then we develop efficient radio RA algorithms to maximize the sum rate of the SUs in each cluster. A user-oriented subchannel assignment method is presented to remove the awkward integer constraints of the formulated RA problem, followed by a fast power distribution algorithm that can work out optimal solutions with an approximate linear complexity. Simulation results indicate that our proposed RA scheme can improve the throughput of the SUs significantly as compared with other methods. Moreover, our proposed RA algorithms converge stably and quickly.

Since user experience plays a more and more important role in the development of today's communication systems, quality of experience (QoE) becomes a widely used metric, which reflects the subjective experience of end users for wireless services. In this paper, a QoE-driven radio resource allocation scheme is proposed for multi-user orthogonal frequency division multiplexing (OFDM) based cognitive radio (CR) networks. By introducing a QoE-based assessment model, our optimization task is formulated to maximize the overall QoE of the considered system subject to the total transmit power and interference constraints. We first propose a user-oriented subcarrier allocation algorithm. Secondly, for a given subchannel assignment, we propose a fast barrier method to tackle the optimal power allocation problem, where the key is to replace Newton step with complexity O(N3) in the barrier method by a procedure with approximate linear complexity, which is developed by exploiting the structure of the optimization problem. Simulation results validate that our method can always reach a better performance in terms of both single user QoE and the overall QoE with lower complexity.