5 papers
Power Allocation for the Base Matrix of Spatially Coupled Sparse Regression Codes
Nian Guo, Shansuo Liang, Wei Han
We investigate power allocation for the base matrix of a spatially coupled sparse regression code (SC-SPARC) for reliable communications over an additive white Gaussian noise chann…
Orthogonal AMP for Problems with Multiple Measurement Vectors and/or Multiple Transforms
Yiyao Cheng, Lei Liu, Shansuo Liang +2
Approximate message passing (AMP) algorithms break a (high-dimensional) statistical problem into parts then repeatedly solve each part in turn, akin to alternating projections. A d…
Capacity-Achieving Sparse Regression Codes via Vector Approximate Message Passing
Yizhou Xu, YuHao Liu, ShanSuo Liang +4
Sparse regression codes (SPARCs) are a promising coding scheme that can approach the Shannon limit over Additive White Gaussian Noise (AWGN) channels. Previous works have proven th…
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
Teng Fu, YuHao Liu, Jean Barbier +3
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular…
A Tensor-BTD-based Modulation for Massive Unsourced Random Access
Zhenting Luan, Yuchi Wu, Shansuo Liang +3
In this letter, we propose a novel tensor-based modulation scheme for massive unsourced random access. The proposed modulation can be deemed as a summation of third-order tensors,…