Syndrome-Enabled Unsupervised Learning for Channel Adaptive Blind Equalizer with Joint Optimization Mechanism

With the rapid growth of deep learning in many fields, machine learning-assisted communication systems has attracted lots of researches with many eye-catching initial results. At the present stage, most of the methods still have great demand of massive "labeled data" for supervised learning to overcome channel variation. However, obtaining labeled data in practical applications may result in severe transmission overheads, and thus degrade the spectral efficiency. To address this issue, syndrome loss has been proposed to penalize non-valid decoded codewords and to achieve unsupervised learning for neural network-based decoder. However, it has not been evaluated under varying channels and cannot be applied to polar codes directly. In this work, by exploiting the nature of polar codes and taking advantage of the standardized cyclic redundancy check (CRC) mechanism, we propose two kinds of modified syndrome loss to enable unsupervised learning for polar codes. In addition, two application scenarios that benefit from the syndrome loss are also proposed for the evaluation. From simulation results, the proposed syndrome loss can even outperform supervised learning for the training of neural network-based polar decoder. Furthermore, the proposed syndrome-enabled blind equalizer can avoid the transmission of training sequences under time-varying fading channel and achieve global optimum via joint optimization mechanism, which has 1.3 dB gain over non-blind minimum mean square error (MMSE) equalizer.