Resource allocation algorithm based on hybrid particle swarm optimization for multiuser cognitive OFDM network

• Model the uplink chance-constrained resource allocation problem.
• We propose HPSO which combines the SVM and PSO.
• Utilize SVM to compute the probabilistic interference constraint condition.

Resource allocation plays a critical role to enhance the performance of cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to lack the cooperation between cognitive network and primary network, the channel state information (CSI) between cognitive radio (CR) user and primary user (PU) could not be estimated precisely. In this work, a resource allocation problem over the power and subcarrier allocation based on chance-constrained programming is formulated to maximize the average weighted sum-rate throughput and guarantee the probabilistic interference constraint condition for PU. In order to solve the above resource allocation problem, the probabilistic interference constraint condition is computed by support vector machine (SVM) and we combine particle swarm optimization (PSO) and SVM to develop hybrid particle swarm optimization (HPSO). Simulation results verify HPSO not only yields the higher average weighted sum-rate throughput than other algorithms, but also satisfies the probabilistic interference constraint condition.