Interleaved Block-Sparse Transform
Lei Liu, Ming Wang, Shufeng Li, Yuhao Chi, Ning Wei, Zhaoyang Zhang · IEEE Communications Letters · 2025
Low-complexity Bayes-optimal memory approximate message passing (MAMP) is an efficient signal estimation algorithm. However, achieving replica Bayes optimality with MAMP necessitates a large-scale right-unitarily invariant transformation, which is prohibitive in practical systems due to its high computational complexity and hardware costs. To solve this problem, this letter proposes a low-complexity interleaved block-sparse (IBS) transform and a corresponding IBS cross-domain memory approximate message passing (IBS-CD-MAMP) estimator applicable to multiple scenarios, which consist of multiple low-dimensional transform matrices interleaved in an innovative manner and leverage various fast algorithms, to reduce the hardware requirements while mitigating performance loss. Numerical results show that our approach reduces the hardware implementation scale to under 10% and complexity by over 50% with excellent performance in the considered large-scale compressed sensing and multicarrier communication scenarios. This offers efficient solutions for resource-constrained scenarios, which are limited by practical hardware scale and complexity constraints in large-scale communication systems.