Genetic Algorithm-Based Joint Channel Estimation and Data Detection For Multi-User Mimo

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Addis Ababa University


To increase the number of users supported by a basestation, current technologies use space division multiple access (SDMA) as one of spectral efficient multiplexing method. Multi-user multiple-input multiple-output (MU-MIMO) is one of the special cases of SDMA which uses different channel estimation (CE) and data detection (DD) methods at the receiver. In this thesis uplink cellular communication system with four simultaneous users, each occupied with a single transmitter antenna and two receiver antennas at the basestation is designed. Transmitted signals from different users are detected at the basestation receiver using their unique user specific spatial signature constituted by their frequency domain channel transfer functions (FD-CHTFs). Accurate channel state information (CSI) is required at the receiver for coherent demodulation and interference cancelation. Due to large number of independent transmitter to receiver links CE is more challenging for MU-MIMO. Genetic algorithm (GA) is used as an effective solution to the MU-MIMO joint CE and DD problem in the above mentioned overloaded scenario. GA-based channel estimator and GA-based data detector are designed independently using selection, crossover, mutation, and replacement operators. Optimum parameter values are selected using observation of change in fitness values with change in parameter values. Performance comparisons between different CE and DD methods are made by using bit error rate (BER) and mean square error (MSE) values at specific signal-to-noise ratio (SNR) points. GA-based joint channel estimation and data detection (JCEDD) is designed by combining GA-based CE and GA-based DD and performance of the receiver is compared with other JCEDD methods. Reliability of the overloaded receiver is further increased by the use of low-density parity-check (LDPC) channel encoder. Key words: MU-MIMO, Channel estimation, Multi-user detection, Genetic algorithm.



MU-MIMO, Channel Estimation, Multi-User Detection, Genetic Algorithm