Ant colony input parameters optimization for multiuser detection in DS/CDMA systems

In this work a simple and efficient methodology for tuning the input parameters applied to the ant colony optimization multiuser detection (ACO-MuD) in direct sequence code division multiple access (DS-CDMA) is proposed. The motivation in using a heuristic approach is due to the nature of the NP complexity posed by the wireless multiuser detection optimization problem. The challenge is to obtain suitable data detection performance in solving the associated hard complexity problem in a polynomial time. Previous results indicated that the application of heuristic search algorithms in several wireless optimization problems have been achieved excellent performance-complexity tradeoffs. Regarding different system operation and channels scenarios, a complete input parameters optimization procedure for the ACO-MuD is provided herein, which represents the major contribution of this work. The performance of the ACO-MuD is analyzed via Monte-Carlo simulations. Simulation results show that, after convergence, the performance reached by the ACO-MuD is much better than the conventional detector, and somewhat close to the single user bound (SuB). Flat Rayleigh channels is initially considered, but the input parameter optimization methodology is straightforward applied to selective fading channels scenarios, as well as to joint time-spatial wireless channels diversities.

► ACO-MuD is able to solve hard complexity problem in a polynomial time. ► Methodology for tuning the input ACO parameters in flat Rayleigh MuD-CDMA. ► This methodology is easily applied to selective fading and time-spatial channels. ► ACO-MuD proved to be robust against NFR, channel mobility and unbalanced powers. ► Very low ACO-MuD complexity under full system loading, wide range NFR and mobility.