Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network

Bagadi, Kala Praveen; Das, Susmita

doi:10.1007/s11277-013-1492-2

Cited by 28 publications

(11 citation statements)

References 27 publications

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“…These neurons pass information and exchange message to each other. Particular numerical weights are assigned to each connection which can be changed to get the desired output depending upon the application [12] [13]. The multilayer perceptron model is described in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Parallel Interference Mitigation Technique using Artificial Neural Network in Complementary Coded MC-CDMA

Bal¹,

Kumar

Kim

et al. 2018

IJFGCN

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Section: Introductionmentioning

confidence: 99%

An Adaptive Parallel Interference Mitigation Technique using Artificial Neural Network in Complementary Coded MC-CDMA

Bal¹,

Kumar

Kim

et al. 2018

IJFGCN

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“…Attractive properties of NNs relevant of the signal detection problem are robustness, finite memory and nonlinear classification ability. Thus, in the recent past, ANNs are extensively utilized as multiuser detectors for space division multiple accessorthogonal frequency division multiplexing (SDMA-OFDM) system achieving better performance than conventional linear techniques [20][21][22][23]. Among various ANNs, the Multilayer Perceptron (MLP) is considered to be simple but powerful tools in the area of pattern classification, where the MLP classifies input pattern with arbitrarily shaped nonlinear decision boundaries [24].…”

Section: Introductionmentioning

confidence: 99%

Receiver Design Using Artificial Neural Network for Signal Detection in Multi Carrier – Code Division Multiple Access System

Chinthaginjala¹,

Bagadi²

2017

IJIES

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Multi carrier-code division multiple access (MC-CDMA) system is a promising wireless communication technology with high spectral efficiency and system performance. Though the multiple access techniques provide high spectral efficiency, these techniques were prone to multiple access interference (MAI). So, this paper mainly aims at the design of the MC-CDMA receiver to mitigate MAI. The classical receivers like maximal ratio combining (MRC), equal gain combining (EGC), and minimum mean square error (MMSE) fails to cancel MAI when the MC-CDMA is subjected to non-linearistic degradations. By contrast, being highly non-linear classifiers, the neural network (NN) receivers could be better alternative under such a case. The feasibility, efficiency and effectiveness of the proposed multilayer perceptron (MLP) NN based receiver are studied in detail for the MC-CDMA with nonlinearistic degradations.

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“…[17][18][19] Thus, for space division multiple access OFDM system, the NN models such as multilayer perceptron (MLP), 20,21 recurrent neural networks (RNN), 22,23 and radial basis function 24,25 network were extensively used as multiuser detectors to achieve better performance over conventional linear techniques. This may impose additional complexity.…”

Section: Introductionmentioning

confidence: 99%

MC–CDMA receiver design using recurrent neural networks for eliminating multiple access interference and nonlinear distortion

Kumar

Bagadi

2017

Int J Communication

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Multicarrier code division multiple access (MC-CDMA) is a promising wireless communication technology with high spectral efficiency and system performance. However, all multiple access techniques including MC-CDMA were most likely to have multiple access interference (MAI). So this paper mainly aims at designing a suitable receiver for MC-CDMA system to mitigate such MAI. The classical receivers like maximal ratio combining, minimum mean square error, and iterative block-decision feedback equalization fail to cancel MAI when the MC-CDMA is subjected to severe nonlinear distortions, which may occur due to saturated power amplifiers or arbitrary channel conditions. Being highly nonlinear structures, the neural network receivers such as multilayer perceptron and recurrent neural network could be better alternative for such a case. The feasibility, efficiency, and effectiveness of the proposed neural network receiver are studied thoroughly for MC-CDMA system under different nonlinear conditions.

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Multiuser Detection in SDMA–OFDM Wireless Communication System Using Complex Multilayer Perceptron Neural Network

Cited by 28 publications

References 27 publications

An Adaptive Parallel Interference Mitigation Technique using Artificial Neural Network in Complementary Coded MC-CDMA

An Adaptive Parallel Interference Mitigation Technique using Artificial Neural Network in Complementary Coded MC-CDMA

Receiver Design Using Artificial Neural Network for Signal Detection in Multi Carrier – Code Division Multiple Access System

MC–CDMA receiver design using recurrent neural networks for eliminating multiple access interference and nonlinear distortion

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