Nonlinear channel modeling and identification using baseband Volterra–Parafac models

Baseband Volterra models are very useful for representing nonlinear communication channels. These models present the specificity to include only odd-order nonlinear terms, with kernels characterized by a double symmetry. The main drawback is their parametric complexity. In this paper, we develop a new class of Volterra models, called baseband Volterra–Parafac models, with a reduced parametric complexity, by using a doubly symmetric Parafac decomposition of high order Volterra kernels viewed as tensors. Three adaptive algorithms are then proposed for estimating the parameters of these models. Some Monte Carlo simulation results are presented to compare the performance of the proposed estimation algorithms, in the case of third-order baseband Volterra systems excited by PSK and QAM inputs.

► The presentation of baseband Volterra models with doubly symmetrized or doubly triangularized high order kernels.
► The introduction of a new Parafac decomposition for doubly symmetric tensors.
► The derivation of baseband Volterra–Parafac models characterized by a reduced parametric complexity.
► The development of ECKF, CLMS and NCLMS algorithms for parameter estimation of baseband Volterra–Parafac models.