A Block Term Decomposition Model Based Algorithm for Tensor Completion of Multidimensional Harmonic Signals

We consider tensor data completion of an incomplete observation of multidimensional harmonic (MH) signals. Unlike existing tensor-based techniques for MH retrieval (MHR), which mostly adopt the canonical polyadic decomposition (CPD) to model the simple "one-to-one" correspondence among har...

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Veröffentlicht in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 1 - 5
Hauptverfasser: Wang, Lei, Gong, Xiao-Feng, Liu, Xi-Yuan, Feng, Wei, Lin, Qiu-Hua
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 06.04.2025
Schlagworte:
block term decomposition
hankelization
Harmonic analysis
Minimization
Multidimensional harmonic retrieval
Numerical models
Optimization
Signal processing
Signal processing algorithms
Speech processing
tensor completion
Tensors
Wireless communication
Wireless sensor networks
ISSN:2379-190X
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Zusammenfassung:We consider tensor data completion of an incomplete observation of multidimensional harmonic (MH) signals. Unlike existing tensor-based techniques for MH retrieval (MHR), which mostly adopt the canonical polyadic decomposition (CPD) to model the simple "one-to-one" correspondence among harmonics across difference modes, we herein use the more flexible block term decomposition (BTD) model that can be used to describe the complex mutual correspondences among several groups of harmonics across different modes. An optimization principle that aims to fit the BTD model in the least squares sense, subject to rank minimization of hankelized MH components, is set up for the tensor completion task, and an algorithm based on alternating direction method of multipliers is proposed, of which the effectiveness and applicability are validated through both numerical simulations and an application in sub-6GHz channel state information (CSI) completion.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10889338