Abstract-CANDECOMP/PARAFAC (CP) analysis is an extension of low-rank matrix decomposition to higher-way arrays, which are also referred to as tensors. CP extends and unifies several array signal processing tools and has found applications ranging from multidimensional harmonic retrieval and angle-carrier estimation to blind multiuser detection. The uniqueness of CP decomposition is not fully understood yet, despite its theoretical and practical significance. Toward this end, we first revisit Kruskal's Permutation Lemma, which is a cornerstone result in the area, using an accessible basic linear algebra and induction approach. The new proof highlights the nature and limits of the identification process. We then derive two equivalent necessary and sufficient uniqueness conditions for the case where one of the component matrices involved in the decomposition is full column rank. These new conditions explain a curious example provided recently in a previous paper by Sidiropoulos, who showed that Kruskal's condition is in general sufficient but not necessary for uniqueness and that uniqueness depends on the particular joint pattern of zeros in the (possibly pretransformed) component matrices. As another interesting application of the Permutation Lemma, we derive a similar necessary and sufficient condition for unique bilinear factorization under constant modulus (CM) constraints, thus providing an interesting link to (and unification with) CP.
In wireless cellular communications, accurate local mean (shadow) power estimation performed at a mobile station is important for use in power control, handoff, and adaptive transmission. Window-based weighted sample average shadow power estimators are commonly used due to their simplicity. In practice, the performance of these estimators degrades severely when the window size deviates beyond a certain range. The optimal window size for window-based estimators is hard to determine and track in practice due to the continuously changing fading environment. Based on a first-order autoregressive model of the shadow process, we propose a scalar Kalman-filter-based approach for improved local mean power estimation, with only slightly increased computational complexity. Our analysis and experiments show promising results.
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