Reduced-complexity near-optimal Ant-Colony-aided multi-user detection for CDMA systems

Reduced-complexity near-maximum-likelihood Ant-Colony Optimization (ACO) assisted Multi-User Detectors (MUDs) are proposed and investigated. The exhaustive search complexity of the optimal detection algorithm may be deemed excessive for practical applications. For example, a Space-Time Block Coded (STBC) two transmit assisted K = 32-user system has to search through the candidate-space for finding the final detection output during 264 times per symbol duration by invoking the Euclidean-distance-calculation of a 64-element complex-valued vector. Hence, a nearoptimal or near-ML MUDs are required in order to provide a near-optimal BER performance at a significantly reduced complexity. Specifically, the ACO assisted MUD algorithms proposed are investigated in the context of a Multi-Carrier DS-CDMA (MC DS-CDMA) system, in a Multi-Functional Antenna Array (MFAA) assisted MC DS-CDMA system and in a STBC aided DS-CDMA system. The ACO assisted MUD algorithm is shown to allow a fully loaded MU system to achieve a near-single user performance, which is similar to that of the classic Minimum Mean Square Error (MMSE) detection algorithm. More quantitatively, when the STBC assisted system support K = 32 users, the complexity imposed by the ACO based MUD algorithm is a fraction of 1 × 10?18 of that of the full search-based optimum MUD. In addition to the hard decision based ACO aided MUD a soft-output MUD was also developed,which was investigated in the context of an STBC assisted DS-CDMA system using a three-stage concatenated, iterative detection aided system. It was demonstrated that the soft-output system is capable of achieving the optimal performance of the Bayesian detection algorithm.