An FPGA-based SOM circuit architecture for online learning of 64-QAM data streams

Responsible for fast transmission and large data volume, Quadrature Amplitude Modulation (QAM) has been applied in many communication systems, which explains the great interest in finding compatible techniques to improve its behavior. Kohonen Self-Organizing Maps (SOM) were implemented in a FPGA circuit to detect 64-QAM symbols. The use of SOM in the Quadrature/In-phase pair detection allowed the continuous adjustment to the QAM constellation with no supervision, as required in some adaptive communication systems. Bandwidth can thus be saved and training data retransmission was no longer necessary. The parallelism provided by FPGA processing showed to be compatible to the state-of-the-art works, presenting a performance of 8.05 GCuPs combined with a low percentage of hardware use (∼7%) In addition, experiments with different physical channels data have been performed and the bit error rates (BER) are reported for several signal to noise ratios.

[1]  B. Mikkelsen,et al.  Practical implementation of higher order modulation beyond 16-QAM , 2015, 2015 Optical Fiber Communications Conference and Exhibition (OFC).

[2]  Emilio Del-Moral-Hernandez,et al.  Comparison of three FPGA architectures for embedded multidimensional categorization through Kohonen's self-organizing maps , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[3]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[4]  Hossein Khoshbin Ghomash,et al.  Performance of Back-propagation and Self Organizing Map Neural Equalizers for Asymmetrically Clipped Optical OFDM , 2013, ICEE 2013.

[5]  Teuvo Kohonen,et al.  Essentials of the self-organizing map , 2013, Neural Networks.

[6]  Charu C. Aggarwal,et al.  On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.

[7]  Witold Pedrycz,et al.  Programmable triangular neighborhood function for kohonen self-organizing map implemented on chip , 2010, Proceedings of the 17th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2010.

[8]  D. Godard,et al.  Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..

[9]  Maria Cristina Felippetto de Castro,et al.  Concurrent blind channel equalization with phase transmittance RBF neural networks , 2007, Journal of the Brazilian Computer Society.

[10]  Marta Kolasa,et al.  An FPGA implementation of the asynchronous programmable neighborhood mechanism for WTM Self-Organizing Map , 2011, Proceedings of the 18th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2011.

[11]  Maria Cristina Felippetto de Castro,et al.  Adaptive Concurrent Equalization Applied to Multicarrier OFDM Systems , 2008, IEEE Transactions on Broadcasting.

[12]  Esam Abdel-Raheem,et al.  Joint MCMA and DD blind equalization algorithm with variable-step size , 2009, 2009 IEEE International Conference on Electro/Information Technology.

[13]  Soowhan Han.  Self-Organizing Map for Blind Channel Equalization , 2010, J. Inform. and Commun. Convergence Engineering.

[14]  Zhi Ding,et al.  Adaptive Filters for Blind Equalization , 2009 .

[15]  Framing structure , channel coding and modulation for digital terrestrial television , 2022 .

[16]  Yutaka Maeda,et al.  Improved Learning Performance of Hardware Self-Organizing Map Using a Novel Neighborhood Function , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[18]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[19]  J. Fort,et al.  On the A.S. Convergence of the Kohonen Algorithm with a General Neighborhood Function , 1995 .

[20]  Jorge Peña,et al.  Digital Hardware Architectures of Kohonen's Self Organizing Feature Maps with Exponential Neighboring Function , 2006, 2006 IEEE International Conference on Reconfigurable Computing and FPGA's (ReConFig 2006).

[21]  Andrew Hunter,et al.  Implementation and Applications of Tri-State Self-Organizing Maps on FPGA , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Volnei A. Pedroni.  Circuit Design and Simulation with VHDL , 2010 .

[23]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[24]  J. L. Holt,et al.  Back propagation simulations using limited precision calculations , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[25]  Akira Hirose,et al.  Predictive self-organizing map for vector quantization of migratory signals and its application to mobile communications , 2003, IEEE Trans. Neural Networks.

[26]  Mohammed A. S. Khalid,et al.  A novel radius-adjusted approach for blind adaptive equalization , 2006, IEEE Signal Processing Letters.

[27]  Rafael Gadea Gironés,et al.  A hardware design of a massive-parallel, modular NN-based vector quantizer for real-time video coding , 2008, Microprocess. Microsystems.

[28]  Jihan Zhu,et al.  FPGA Implementations of Neural Networks - A Survey of a Decade of Progress , 2003, FPL.

[29]  Emilio Del-Moral-Hernandez,et al.  OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects , 2017, Neural Computing and Applications.

[30]  Emilio Del-Moral-Hernandez,et al.  An FPGA distributed implementation model for embedded SOM with on-line learning , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).