%0 Journal Article %1 santanukumarsahoo2013adaptive %A Santanu Kumar Sahoo, Sidhartha Dash %A Mohanty, Mihir Narayan %D 2013 %E Das, Dr. Vinu V %J ACEEE International Journal on Control System and Instrumentation %K FLANN ISI %N 2 %P 5 %T Adaptive Channel Equalization for Nonlinear Channels using Signed Regressor FLANN %U http://searchdl.org/public/journals/2013/IJCSI/4/2/1177.pdf %V 4 %X Wireless communication systems are affected by inter-symbol interference (ISI), co-channel interference in the presence of additive white Gaussian noise. ISI is primarily due to the distortion caused by frequency and time selectivity of the fading channel and it causes performance degradation. Equalization techniques are used to mitigate the effect of ISI and noise for better demodulation. This paper presents a novel technique for channel equalization. Here a Signed Regressor adaptive algorithm based on FLANN (Functional Link Artificial Neural Network) has been developed for nonlinear channel equalization along with the analysis of MSE and BER. The results are compared with the conventional adaptive LMS algorithm based FLANN model. The Signed Regressor FLANN shows better performance as compared to LMS based FLANN. The equalizer presented shows considerable performance compared to the other adaptive structure for both the linear and non-linear models in terms of convergence rate, MSE and BER over a wide range.

@article{santanukumarsahoo2013adaptive, abstract = {Wireless communication systems are affected by inter-symbol interference (ISI), co-channel interference in the presence of additive white Gaussian noise. ISI is primarily due to the distortion caused by frequency and time selectivity of the fading channel and it causes performance degradation. Equalization techniques are used to mitigate the effect of ISI and noise for better demodulation. This paper presents a novel technique for channel equalization. Here a Signed Regressor adaptive algorithm based on FLANN (Functional Link Artificial Neural Network) has been developed for nonlinear channel equalization along with the analysis of MSE and BER. The results are compared with the conventional adaptive LMS algorithm based FLANN model. The Signed Regressor FLANN shows better performance as compared to LMS based FLANN. The equalizer presented shows considerable performance compared to the other adaptive structure for both the linear and non-linear models in terms of convergence rate, MSE and BER over a wide range.}, added-at = {2014-01-08T08:10:59.000+0100}, author = {Santanu Kumar Sahoo, Sidhartha Dash and Mohanty, Mihir Narayan}, biburl = {https://www.bibsonomy.org/bibtex/2df55d2912b235210dd6dd9fbda68eb21/ideseditor}, editor = {Das, Dr. Vinu V}, interhash = {1508b4b15b0573ec2171716bbc826648}, intrahash = {df55d2912b235210dd6dd9fbda68eb21}, journal = {ACEEE International Journal on Control System and Instrumentation}, keywords = {FLANN ISI}, month = {June}, number = 2, pages = 5, timestamp = {2014-01-08T08:10:59.000+0100}, title = {Adaptive Channel Equalization for Nonlinear Channels using Signed Regressor FLANN}, url = {http://searchdl.org/public/journals/2013/IJCSI/4/2/1177.pdf}, volume = 4, year = 2013 }