Due to the computational complexity of the optimum maximum likelihood detector (OMD) growing exponentially with the number of users, suboptimum techniques have received significant attention. We have proposed the particle swarm optimization (PSO) for the multiuser detection (MUD) in asynchronous multicarrier code division multiple access (MC-CDMA) system. The performance of PSO based MUD is near optimum, while its computational complexity is far less than OMD. Performance of PSO-MUD has also been shown to be better than that of genetic algorithm based MUD (GA-MUD) at practical SNR.

The computation involved in multiuser detection (MUD) for multicarrier CDMA (MC-CDMA) based on maximum likelihood (ML) principle grows exponentially with the number of users. Particle swarm optimization (PSO) with soft decisions has been proposed to mitigate this problem. The computational complexity of PSO, is comparable with genetic algorithm (GA), but is much less than the optimal ML detector and yet its performance is much better than GA.

The computational complexity of the optimum maximum likelihood detector (OMLD) does not allow its utility for multi-user detection (MUD) in code division multiple access (CDMA) systems. As proposed in this letter, particle swarm optimization (PSO) with soft decision offers a much more efficient option with few parameters to be adjusted, flexibility to implement, that gives a much faster convergence compared to OMLD. It outperforms the conventional detector, the genetic algorithm approach and the standard suboptimal detectors considered in the literature.

In this work particle swarm optimization (PSO) aided with radial basis functions (RBF) has been suggested to carry out multiuser detection (MUD) for synchronous direct sequence code division multiple access (DS-CDMA) systems. The performance of the proposed algorithm is compared to that of other standard suboptimal detectors and genetic algorithm (GA) assisted MUD. It is shown to offer better performance than the others especially if there are many users.

The task of joint channel and data estimation based on the maximum likelihood principle is addressed using a continuous and discrete particle swarm optimization (PSO) algorithm over additive white Gaussian noise channels. The PSO algorithm works at two levels. At the upper level continuous PSO estimates the channel while at the lower level, discrete PSO detects the data. Simulation results indicate that under the same conditions, PSO outperforms the best of the published alternatives.