Additive non-Gaussian noise processes and nonlinear channel characteristics are found to present a performance bottleneck for next-generation communication systems, since most classical links are currently designed with Gaussian and linear system assumption. The joint effect of these impairments not only make channel characterization difficult, but also make optimal receiver design non-trivial and complex. In such scenarios, the joint application of reproducing kernel Hilbert space (RKHS) based machine learning methods, and information-theoretic learning (ITL) criteria, allows for near-optimal mitigation of channel nonlinearity and non-Gaussian noise for upcoming beyond 5G systems. Across different deployment scenarios, RKHS based learning allows for hyperparameter independence, improves generalization in the low-data regime and complements other existing signal processing solutions such as deep learning. Also, existing ITL criteria based algorithms are found to deliver improved performance for machine learning over non-Gaussian additive noise scenarios. Furthermore, existing analytical frameworks for these RKHS based approaches readily allow for the extension of classical performance analysis methods; existing works report rigorous analytical results, and the near-optimality of RKHS based approaches. This paper highlights the benefits and the potential of hyperparameter free RKHS and ITL based methods for simple and generic mitigation of unknown underlying hardware impairments and noise processes.

In the context of learning and inference for non-Gaussian additive noise processes, several non-Bussgang learning criteria have emerged, such as, the maximum correntropy, minimum error entropy, and the maximum Versoria criterion. However, these existing learning criteria are known to depend on hyperparameters such as the shape and the spread parameters, which make these approaches susceptible to arbitrary hyperparameter choices. This work proposes an online hyperparameter free criterion learning algorithm that comprehensively alleviates dependence on hyperparameter choices and self-adapts to underlying noise distributions. For the proposed hyperparameter-free criterion learning, analytical results are derived, and case-studies are presented for its validation.