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Published in 2018 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2018.2818062
Abstract: Graph signal processing (GSP) studies signals that live on irregular data kernels described by graphs. One fundamental problem in GSP is sampling—from which subset of graph nodes to collect samples in order to reconstruct a…
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Keywords:
reconstruction;
truncated neumann;
neumann series;
graph signals ... See more keywords
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1
Published in 2018 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2017.2761876
Abstract: Neumann series (NS) expansion-based precoder in massive multiple input multiple output systems suffers from slow convergence. To solve this problem, this letter proposes a weighted NS expansion precoder. The weights are designed to minimize the…
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Keywords:
series precoding;
converging weighted;
neumann series;
fast converging ... See more keywords
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Published in 2023 at "IEEE Transactions on Circuits and Systems I: Regular Papers"
DOI: 10.1109/tcsi.2022.3229690
Abstract: Expectation propagation (EP) achieves near-optimal performance for large-scale multiple-input multiple-output (L-MIMO) detection, however, at the expense of unaffordable matrix inversions. To tackle the issue, several low-complexity EP detectors have been proposed. However, they all fail…
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Keywords:
neumann series;
diagonal neumann;
block diagonal;
expectation propagation ... See more keywords
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Published in 2021 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2020.3017381
Abstract: Expectation propagation (EP) achieves near-optimal performance for massive multiple-input multiple-output (MIMO) detection, however, at the cost of multiple expensive matrix inversions. An EP with approximation (EPA) algorithm has been introduced to address this issue, which…
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Keywords:
expectation propagation;
neumann series;
weighted neumann;
detector ... See more keywords