LAUSR

This work proposes a modified version of an emerging nature-inspired technique, named flower pollination algorithm, for equalizing digital multiuser channels. This equalization involves two different tasks: (1) estimation of the channel impulse response, and (2) estimation of the users’ transmitted symbols. The new algorithm is developed and applied in a direct sequence/code-division multiple-access multiuser communications system. Important issues such as robustness, convergence speed and population diversity control have been in deep investigated. A method based on the entropy of the flowers’ fitness is proposed for in-service monitoring and adjusting population diversity. Numerical simulations analyze the performance, showing comparisons with well-known conventional multiuser detectors such as matched filter, minimum mean square error estimator or several Bayesian schemes, as well as with other nature-inspired strategies. Numerical analysis shows that the proposed algorithm enables transmission at higher symbol rates under stronger fading and interference conditions, constituting an attractive alternative to previous algorithms, both conventional and nature-inspired, whose performance is frequently sensible to near–far effects and multiple-access interference problems. These results have been validated by running hypothesis tests to confirm statistical significance.