OFDM symbol identification by an unsupervised learning system under dynamically changing channel effects

Orthogonal frequency-division multiplexing (OFDM) is one of the most successful digital communication techniques. Nevertheless, the decrease in inter-symbol interference in quadrature amplitude modulation (QAM) over dispersive channels is still challenging. Different researches recently proposed the idea of using unsupervised learning as an alternative to the classic approaches to equalization of OFDM channels. In those purposes, the identification of a received QAM symbol is possible by the comparison of its position on the in-phase/quadrature (IQ) plane relative to the positions of previously arrived symbols, generally processed by the Kohonen’s Self-Organizing Map (SOM) algorithm. This work presents the SOM unsupervised learning method executed on an embedded system applied to QAM symbols identification. The system is implemented on an FPGA, a configurable digital circuit able to meet the low power and parallel process requirements of mobile applications. Also, in order to extend the classical set of experiments to evaluate our system, this paper proposes a theoretical model of the time-varying scheme representing the transition between different channel characteristics, obtained from real measurements available on a public repository. The model is employed to verify our purpose under dynamically both changing and realistic conditions. On the assumption that it is provided enough IQ symbols for the initial training process, the hardware implementation of SOM is able to track and identify the time-varying distorted QAM constellation. No knowledge of channel characteristics is necessary. The system spends only some microseconds at start-up to reach about 100% performance, and no dedicated training phase is needed afterward.