Reverse Order Law for the Core Inverse in Rings

In this paper, necessary and sufficient conditions of the onesided reverse order law (ab)((sic)) = b((sic))a((sic)) , the two-sided reverse order law (ab)((sic)) = b((sic))a((sic)) and (ba)((sic)) = a((sic))b((sic)) for the core inverse are given in rings with involution. In addition, the mixed-type reverse order laws, such as (ab)((sic)) = b((sic))(abb((sic)))((sic)) , a((sic)) = b(ab)((sic)) and (ab)((sic)) = b((sic)) a((sic)) , are also considered.- This research was supported by China Postdoctoral Science Foundation (No. 2018M632385), the National Natural Science Foundation of China (No. 11771076), the Natural Science Foundation of Jiangsu Province (No. BK20141327), the Portuguese Funds through FCT-"Fundacao para a Ciencia e a Tecnologia", within the project UID/MAT/00013/2013

Global Modeling and Prediction of Computer Network Traffic

We develop a probabilistic framework for global modeling of the traffic over a computer network. This model integrates existing single-link (-flow) traffic models with the routing over the network to capture the global traffic behavior. It arises from a limit approximation of the traffic fluctuations as the time--scale and the number of users sharing the network grow. The resulting probability model is comprised of a Gaussian and/or a stable, infinite variance components. They can be succinctly described and handled by certain 'space-time' random fields. The model is validated against simulated and real data. It is then applied to predict traffic fluctuations over unobserved links from a limited set of observed links. Further, applications to anomaly detection and network management are briefly discussed

Dealing with Interference in Distributed Large-scale MIMO Systems: A Statistical Approach

This paper considers the problem of interference control through the use of second-order statistics in massive MIMO multi-cell networks. We consider both the cases of co-located massive arrays and large-scale distributed antenna settings. We are interested in characterizing the low-rankness of users' channel covariance matrices, as such a property can be exploited towards improved channel estimation (so-called pilot decontamination) as well as interference rejection via spatial filtering. In previous work, it was shown that massive MIMO channel covariance matrices exhibit a useful finite rank property that can be modeled via the angular spread of multipath at a MIMO uniform linear array. This paper extends this result to more general settings including certain non-uniform arrays, and more surprisingly, to two dimensional distributed large scale arrays. In particular our model exhibits the dependence of the signal subspace's richness on the scattering radius around the user terminal, through a closed form expression. The applications of the low-rankness covariance property to channel estimation's denoising and low-complexity interference filtering are highlighted.Comment: 12 pages, 11 figures, to appear in IEEE Journal of Selected Topics in Signal Processin

The reverse order law for the weighted least square g-inverse of multiple matrix products

By using the ranks of the generalized Schur complement, the equivalent conditions for reverse order laws of the $\{1, 3M\}-$ and the $\{1, 4N\}-$ inverses of the multiple product of matrices are derived