In this letter, a sparse Kronecker-product (SKP) coding scheme is proposed for unsourced multiple access. Specifically, the data of each active user encoded as Kronecker product two component codewords with one being and other forward-error-correction (FEC) coded. At receiver, an iterative decoding algorithm developed, consisting matrix factorization decomposition soft-in soft-out code FEC code...

The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding has demonstrated impressive performance on a variety of supervised tasks, but its generalization properties have not been studied. We...

Sparse coding aims to model data vectors as sparse linear combinations of basis elements, but a majority related studies are restricted continuous without spatial or temporal structure. A new model-based (MSC) method is proposed provide an effective and flexible framework for learning features from different types: continuous, discrete, categorical, modeling types correlations: temporal. The sp...