In this paper, a distributed adaptive algorithm for sparsity-aware learning in diffusion networks is developed. The algorithm follows the greedy roadmap for sparsity along with the adapt-combine co-operation strategy, based on the LMS rationale for adaptivity. A bound on the error norm between the obtained estimates and the target vector is computed, and the algorithm is shown to converge in the mean under some general assumptions. Finally, comparative experiments with a recently developed sparsity-promoting diffusion LMS demonstrate the enhanced performance of the proposed algorithm.

This paper is concerned with the blind identification of passband and baseband Hammerstein channels using cumulants of the received sequence. The channel is excited by common communication signals such as QAM, PSK and OFDM; sparseness of the higher order cumulant lags is exploited. Exact expressions and algorithms involving the output cumulants are developed. Performance is assessed by simulations.

Nonparametric identification of baseband and passband complex Volterra systems excited by communication inputs (PSK, QAM and OFDM) is considered. Closed form expressions are established using multidimensional orthogonal polynomials and higher order statistics. First multidimensional orthogonal polynomials are used for baseband and passband Volterra models driven by PSK and QAM inputs and closed form expressions are derived. Baseband Volterra models excited by IID circular Gaussian signals (OFDM) are identified using cross-cumulants. Performance is assessed by simulations.