We consider the identifiability issue of maximum-likelihood based activity detection in massive MIMO-based grant-free random access. An intriguing observation by Chen et al. [1] indicates that the identifiability unde...
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Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT). However, the performance of conventional energy beamforming may seriously degrade ...
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Manifold optimization is ubiquitous in computational and applied mathematics, statistics, engineering, machine learning, physics, chemistry and etc. One of the main challenges usually is the non-convexity of the manif...
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A promising approach to deal with the high hardware cost and energy consumption of massive MIMO transmitters is to use low-resolution digital-to-analog converters (DACs) at each antenna element. This leads to a transm...
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In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of d agents. The objective is an average of d local functions, and each function is ...
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PHG (parallel hierarchical grid) is a scalable parallel adaptive finite element toolbox under active developmentat the statekeylaboratory of scientific and engineeringcomputing, Chinese Academy of Sciences. This pa...
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PHG (parallel hierarchical grid) is a scalable parallel adaptive finite element toolbox under active developmentat the statekeylaboratory of scientific and engineeringcomputing, Chinese Academy of Sciences. This paper demonstrates its application to adaptive finite element computations of electromagnetic problems. Two examples on solving the time harmonic Maxwell's equations are shown. Results of some large scale adaptive finite element simulations with up to 1 billion degrees of freedom and using up to 2048 CPUs are presented.
The proximal alternating linearized minimization method (PALM) suits well for solving blockstructured optimization problems, which are ubiquitous in real applications. In the cases where subproblems do not have closed...
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Decentralized optimization with orthogonality constraints is found widely in scientificcomputing and data science. Since the orthogonality constraints are nonconvex, it is quite challenging to design efficient algori...
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To construct a parallel approach for solving optimization problems with orthogonality constraints is usually regarded as an extremely difficult mission, due to the low scalability of the orthonormalization procedure. ...
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