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Sphere decoding (SD) is an efficient algorithm for Multiple-input Multiple-output (MIMO) digital communications. It has been showed to achieve near Maximum Likelihood (ML) performance with low complexity. However, the complexity of conventional SD algorithm is high under the specific environment. The performance and the computation complexity of SD algorithm is highly dependent on the initial choice...
We consider three kinds of minimum single source shortest path tree expansion problems. Given an undirected connected graph G = (V, E; w, c, b; s) with n vertexes, m edges and a positive constant H, w(e) is the length of edge e, c(e) is the capacity of edge e, b(e) is the unit cost to increase the capacity of edge e, H is a given capacity restriction value and s is a fixed vertex of G. For every edge...
Sphere decoding(SD)is an efficient algorithm which has been proposed in Multiple input Multiple output (MIMO) digital communication. Sphere decoding algorithm is based on the rule of maximum likelihood decoding algorithm, But SD algorithm does not like ML algorithm to retrieve all of the lattice. However, SD in some environment complexity is very high. The complexity of the SD is controlled by radius...
Bit-interleaved coded modulation with iterative decoding (BICM-ID) can achieve better performance than BICM in both AWGN and Rayleigh fading channels because of the feedback algorithm. However, some existing decoding schemes in BICM-ID cannot perform well in both computational complexity and decoding performance. The Max-Log-MAP algorithm has lower computational complexity with worse decoding performance...
Motivated by various network improvement models, we study the problem to add some new edges to satisfy the increasing information demand and keep the underlying structure of the networks unchanged. In this paper we propose the general network expansion problem on the spanning tree in graphs (GNEST), then we present the polynomial equivalence between the GNEST problem and the constrained minimum spanning...
Least squares support vector machine (LS-SVM) has an outstanding advantage of lower computational complexity than that of standard support vector machines. Its shortcomings are the loss of sparseness and robustness. Thus it usually results in slow testing speed and poor generalization performance. In this paper, a least squares support vector machine with linear programming formulation (LS-SVM-LP)...
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