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MIMO Channel Equalization and Symbol Detection using Multilayer Neural Network
Blekinge Institute of Technology, School of Engineering.
Blekinge Institute of Technology, School of Engineering.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

In recent years Multiple Input Multiple Output (MIMO) systems have been employed in wireless communication systems to reach the goals of high data rate. A MIMO use multiple antennas at both transmitting and receiving ends. These antennas communicate with each other on the same frequency band and help in linearly increasing the channel capacity. Due to the multi paths wireless channels face the problem of channel fading which cause Inter Symbol Interference (ISI). Each channel path has an independent path delay, independent path loss or path gain and phase shift, cause deformations in a signal and due to this deformation the receiver can detect a wrong or a distorted signal. To remove this fading effect of channel from received signal many Neural Network (NN) based channel equalizers have been proposed in literature. Due to high level non-linearity, NN can be efficient to decode transmitted symbols that are effected by fading channels. The task of channel equalization can also be considered as a classification job. In the data (received symbol sequences) spaces NN can easily make decision regions. Specifically, NN has the universal approximation capability and form decision regions with arbitrarily shaped boundaries. This property supports the NN to be introduced and perform the task of channel equalization and symbol detection. This research project presents the implementation of NN to be use as a channel equalizer for Rayleigh fading channels causing ISI in MIMO systems. Channel equalization has been done using NN as a classification problem. The equalizer is implemented over MIMO system of different forms using Quadrature Amplitude Modulation scheme (4QAM & 16QAM) signals. Levenberg-Marquardt (LM), One Step Secant (OSS), Gradient Descent (GD), Resilient backpropagation (Rprop) and Conjugate Gradient (CG) algorithms are used for the training of NN. The Weights calculated during the training process provides the equalization matrix as an estimate of Channel. The output of the NN provides the estimate of transmitted signals. The equalizer is assessed in terms of Symbol Error Rate (SER) and equalizer efficiency.

Place, publisher, year, edition, pages
2013. , 91 p.
Keyword [en]
MIMO, ISI, NN, SER, Channel Equalizer, QAM
National Category
Signal Processing Computer Science Telecommunications
URN: urn:nbn:se:bth-2345Local ID: diva2:829615
Available from: 2015-04-22 Created: 2013-09-18 Last updated: 2015-06-30Bibliographically approved

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