复旦大学数字信号处理与传输实验室的金麟同学的论文A Sufficient Lower Bound for Sampling of Multiple Correlated Stochastic Signals，被IEEE Sensor Array and Multichannel Signal Processing Workshop (IEEE SAM 2026) 录用

论文摘要：Multiple stochastic signals received in array or MIMO systems may have inherent statistical correlations, yet conventional sampling on each channel independently result in data redundancy. To leverage such correlation for efficient sampling, we model correlated signals as a linear combination of a smaller set of uncorrelated, wide-sense stationary (WSS) latent sources. We establish a sufficient lower bound on the total sampling density for zero mean-square error reconstruction. We then develop a constructive multi-band sampling scheme to achieve this theoretical bound. The proposed method operates via spectral partitioning, followed by spatio-temporal sampling and interpolation. Experiments on synthetic and real datasets confirm that our scheme achieves near-lossless reconstruction precisely at the theoretical sampling density.

论文作者：Lin Jin, Hang Sheng, Hui Feng, Bo Hu