An Iterative Approach to Syndrome-based Deep Learning Decoding
Emmanouil Kavvousanos, Vassilis Paliouras · 2020
Recently, Machine Learning has been considered for use in various communications subsystems, providing an alternative paradigm for addressing core problems in the physical (PHY) and medium access control (MAC) layers. Several methods and neural network topologies have been proposed for channel decoding targeting short and moderate length codes. In this paper, we consider the Syndrome-based Deep Learning Decoder and propose a modification in order to enable iterative operation. The proposed modifications perform iterations of the original decoder, which are unrolled to build a larger network. The unrolled iterations are subsequently trained jointly. Moreover, the proposed iterative functionality is incorporated within the learning process by accounting for the error of each iteration in the loss function. By sharing the weights between the iterations, we show that it is possible to reduce the storage requirements of the original decoder by a factor of 2.6x for BCH(63,45), while approaching maximum-likelihood decoding performance. Finally, simulation results are provided for several code lengths and rates, which demonstrate the error-correcting capability of the proposed iterative decoder.