Turbo Coding, Turbo Equalisation and Space-Time Coding for Transmission over Fading Channels
Turbo Coding, Turbo Equalisation and Space-Time Coding for Transmission over Fading Channels
Multiuser Detection
OFDM and MC-CDMA for Broadband Multi-User Communications, WLANs and Broadcasting
OFDM and MC-CDMA for Broadband Multi-User Communications, WLANs and Broadcasting
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
Multiuser MIMO OFDM Based TDD/TDMA for Next Generation Wireless Communication Systems
Wireless Personal Communications: An International Journal
A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems
IEEE Transactions on Wireless Communications
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
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In this paper, we propose two novel and computationally efficient metaheuristic algorithms based on Artificial Bee Colony (ABC) and Tabu Search (TS) principles for Multi User Detection (MUD) in Turbo Trellis Coded Modulation based Space Division Multiple Access Orthogonal Frequency Division Multiplexing system. Unlike gradient descent methods, both ABC and TS methods ensure minimization of the objective function without the solution being trapped into local optima. These techniques are capable of achieving excellent performance in the so called overloaded system, where the number of transmit antennas is higher than the number of receiver antennas, in which the known classic MUDs fail. The performance of the proposed algorithms are compared with each other and also against Genetic Algorithm (GA) and K-Best sperical decoding algorithm based MUD. Simulation results establish better performance, computational efficiency and convergence characteristics for ABC and TS methods. It is seen that the proposed detectors achieve similar performance to that of well known optimum Maximum Likelihood Detector (MLD) at a significantly lower computational complexity and outperforms the traditional MMSE MUD.