Large scale (LS) MIMO orthogonal frequency division multiplexing (OFDM) technique satisfies the demands on performance and the service quality preferred in wireless communication system. Since numerous antenna terminals have been incorporated in the base station, multiuser detection is crucial for retrieving the data appropriately. Thus, the complexities of the detectors increase rapidly in large scale MIMO OFDM schemes. A unique neural network detection approach for multiuser detection in LS MIMO OFDM model is presented. The performance of multilayer perceptron (MLPNN) and radial basis (RBNN) detectors with minimum mean square (MMSE) MUDs are analyzed. The neural network multiuser detecting methodologies are also analyzed by the loss function in LS MIMO OFDM systems. The performance analyses of the detectors are also done by analyzing the radiation patterns of the antennas which is equipped at the both ends of the uplink channels. Radial basis MUDs have 23 dB bit error rate which gains over linear MMSE detector and of 14 dB than multilayer perceptron detectors specifically at 10 (À5) bit error rate. According to the bit error rate analysis of estimations at the output of the multiuser detector, the suggested neural network models are more effective in reducing multiuser interference in MU LS MIMO OFDM. Analysis also verifies, radial basis function (RBF) multiuser detector are exhibiting an optimal BER performance than multilayer perceptron detector with sufficient number of hidden neurons. This study thus emphasis a RBF MUD scheme for detecting multiuser signals in LS MIMO OFDM system due to its dynamic performance in low probability of bit error.
Background:
Large-scale MIMO OFDM technique satisfies the demands on performance
and the service quality preferred in wireless communication systems. Since numerous antenna terminals have been incorporated in the base station, multiuser detection is crucial for retrieving the data
appropriately. Thus, the complexities of the detectors increase rapidly in large-scale MIMO OFDM
schemes.
Objective:
This work is a solution to achieve an extensively high rate of data transmission, which will
help improve the capacity of the LS MIMO OFDM system.
Methods:
A unique detection approach of multiuser detection in LS MIMO OFDM model with channel coding, like low density parity check codes (LDPC), is suggested in this paper. The LDPC-coded
large-scale MIMO OFDM system has also been analysed in the study with users of around ten at the
transmitter and several antennas in the base station.
Results:
BER of the LDPC-coded LS MIMO OFDM exhibited a waterfall region for SNR greater
than 6dB as the study has been done with different decoding iterations. The BER performance
worsened with the increase in modulation symbols. The study has shown how the BER performance
has improved with respect to the increasing fading channels and subcarriers.
Conclusion:
The proposed system exhibited performance closer to the MIMO capacity with low
complexity MMSE detection. The multiuser detector of LDPC-coded LS MIMO OFDM has been
analysed by error rate in received bits (BER) with respect to different parameters, such as modulation
orders, iteration values, receiving antennas, and OFDM subcarriers.
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