Robust Multidimensional Chinese Remainder Theorem (MD-CRT) with Non-Diagonal Moduli and Multi-Stage Framework
Guangpu Guo, Xiang-Gen Xia

TL;DR
This paper extends the Chinese remainder theorem to multidimensional cases with non-diagonal moduli, demonstrating improved robustness and conditioning, and introduces a multi-stage framework for enhanced error tolerance in signal processing.
Contribution
It investigates the advantages of non-diagonal moduli in multidimensional CRT and proposes a multi-stage robust MD-CRT framework for better error resilience.
Findings
Non-diagonal matrices provide more balanced sampling patterns.
Longer shortest vectors in lattices improve robustness to errors.
Multi-stage robust MD-CRT outperforms single-stage in error tolerance.
Abstract
The Chinese remainder theorem (CRT) provides an efficient way to reconstruct an integer from its remainders modulo several integer moduli, and has been widely applied in signal processing and information theory. Its multidimensional extension (MD-CRT) generalizes this principle to integer vectors and integer matrix moduli, enabling reconstruction in multidimensional signal processing scenarios. However, since matrices are generally non-commutative, the multidimensional extension introduces new theoretical and algorithmic challenges. When all matrix moduli are diagonal, the system is equivalent to applying the one-dimensional CRT independently along each dimension. This work first investigates whether non-diagonal (non-separable) moduli offer fundamental advantages over traditional diagonal ones. We show that under the same determinant constraint, non-diagonal matrices do not increase…
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