Deep-Learning for Generic Blind-Joint Channel Equalization and Power Amplifier Post-Distortion

One of the major problems faced in digital communication systems is Inter-Symbol Interference (ISI), induced by the propagation channel in the single carrier based systems. Classic digital equalization techniques based on pilot training sequences become tedious in the presence of nonlinear power amplifiers. The existing techniques for digital pre-distortion need a high transmitter computation complexity and those for the post-distortion require pilot overhead. In this review, we focus on fully generic blind processing for both channel equalization and power amplifier post-distortion. We propose a receiver based on two complex-valued neural networks (CV-NN). The first CV-NN is dedicated to generic blind equalization (GBE) to mitigate ISI. The second one is used for generic blind post-distortion compensation (GBPDC) of the power amplifier nonlinearity (PANL). The GBE and the GBPDC have no prior information about the transmission channel, the used constellation, and the PA model. For the first CV-NN, we consider an updated probability density fitting (PDF) based criteria, corresponding to many assumed possible constellations, that are used jointly with an automatic modulation classification (AMC) based on the k-nearest neighbors (KNN) algorithm. For the second CV-NN, we use the final updated PDF criterion resulting from the first CV-NN training process. Numerical results show that our generic blind Deep learning-based signal receiver formed with the two CV-NNs is effective in alleviating the two coupled signal distortions: the ISI and the PANL. Compared with the state-of-the-art methods, banking on a supervised post-distortion compensation and channel equalization, the proposed generic blind DL-based scheme exhibits good detection performance.