International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 48 - Issue 4 |
Published: June 2012 |
Authors: Gargi Kar Purkayastha, Kandarpa Kumar Sarma |
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Gargi Kar Purkayastha, Kandarpa Kumar Sarma . Maximal Ratio Combining using Self Organizing Map in Wireless Channels. International Journal of Computer Applications. 48, 4 (June 2012), 32-37. DOI=10.5120/7339-0161
@article{ 10.5120/7339-0161, author = { Gargi Kar Purkayastha,Kandarpa Kumar Sarma }, title = { Maximal Ratio Combining using Self Organizing Map in Wireless Channels }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 48 }, number = { 4 }, pages = { 32-37 }, doi = { 10.5120/7339-0161 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A Gargi Kar Purkayastha %A Kandarpa Kumar Sarma %T Maximal Ratio Combining using Self Organizing Map in Wireless Channels%T %J International Journal of Computer Applications %V 48 %N 4 %P 32-37 %R 10.5120/7339-0161 %I Foundation of Computer Science (FCS), NY, USA
The work is related to the use of Self Organizing Map (SOM) which is a type of unsupervised Artificial Neural Network (ANN), as an aid to Maximal Ratio Combining (MRC) in order to improve bit error rate (BER) values of demodulated signals in wireless channels that have both Gaussian and multipath fading characteristics. Among the architectures and algorithms suggested for ANN, the SOM has the special property of effectively creating spatially organised " internal representations" of various features of input signals and their abstractions. The advantage of using the SOM is that it doesn't require any reference signal for training. Modulation technique used in this work is Bipolar Phase Shift Keying (BPSK) in Gaussian and multipath Rayleigh fading channels. The work adopts ANN block as part of a MRC set-up and is tested under SNR variation between -10 to 10 dB in Gaussian and multipath fading channels. The results generated justify the use of SOM neural network block as an aid to the MRC setup.