18 citations · 66 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?
Jean Barbier, TianQi Hou, Marco Mondelli +1
We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms per…
Sparse superposition codes with rotational invariant coding matrices for memoryless channels
YuHao Liu, Teng Fu, Jean Barbier +1
We recently showed in [1] the superiority of certain structured coding matrices ensembles (such as partial row-orthogonal) for sparse superposition codes when compared with purely…
Sparse superposition codes under VAMP decoding with generic rotational invariant coding matrices
TianQi Hou, YuHao Liu, Teng Fu +1
Sparse superposition codes were originally proposed as a capacity-achieving communication scheme over the gaussian channel, whose coding matrices were made of i.i.d. gaussian entri…