2017
DOI: 10.1109/tvt.2016.2622010
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An Eigen-Based Approach for Enhancing Matrix Inversion Approximation in Massive MIMO Systems

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Cited by 14 publications
(4 citation statements)
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“…If the vertex is denoted by V of G and if the subset of vertices is denoted by P in one component of G induced by cut set, then P, P represents the cut set, where P = V − P. Dimension of the n vertices for Cut set space of a graph is (n − 1). By utilizing these, the final Laplacian matrix will be denoted as L G [40][41][42].…”
Section: Representation Of the Networkmentioning
confidence: 99%
“…If the vertex is denoted by V of G and if the subset of vertices is denoted by P in one component of G induced by cut set, then P, P represents the cut set, where P = V − P. Dimension of the n vertices for Cut set space of a graph is (n − 1). By utilizing these, the final Laplacian matrix will be denoted as L G [40][41][42].…”
Section: Representation Of the Networkmentioning
confidence: 99%
“…A set of old vertices V g with possible unvisited edges are stored in memory [32,33]. The updated Laplacian matrix then can be obtained as [32,34,35]. The following Lemmas elaborate the formation of cut-sets: Lemma 1.…”
Section: Depth First Search Algorithmmentioning
confidence: 99%
“…Essentially, the in-degree matrices have a greater effect on the global dynamics of the node, which is influenced by its neighbors. In a practical system, if the graph is undirected (which means all the links are bi-directional), then the Laplacian matrix is balanced [30][31][32].…”
Section: Review Of Graph Theorymentioning
confidence: 99%