Hyperparameter Free MEEF-Based Learning for Next Generation Communication Systems

Information theoretic learning (ITL) criteria have emerged useful for mitigating degradations caused by unknown non-Gaussian noise processes in future wireless communication systems. Specifically, the reproducing kernel Hilbert space (RKHS) based approaches relying on ITL based learning criteria are envisioned to provide near-optimal mitigation of unknown hardware impairments and non-Gaussian noises. Among several ITL criteria, the recent works find the minimum error entropy with fiducial points (MEEF) promising due to its guarantee of unbiased estimation and generalization over generic noise distributions. However, MEEF based learning approaches are known to depend on an accurate kernel-width initialization. Also, the optimal value of this kernel-width is well-known to vary temporally and across deployment scenarios. To remove the dependency on kernel-width, a hyperparameter-free MEEF based adaptive algorithm is derived using random-Fourier features with sampled kernel widths (RFF-SKW). In addition, a detailed convergence analysis is presented for the proposed hyperparameter-free MEEF, which promises a near-optimal error-floor independent of step-size and guarantees convergence for a wide range of step sizes. The promised hyperparameter-independence and improved convergence for the proposed hyperparameter-free MEEF are validated by computer simulations considering different case studies.