42 citations · 84 across the 5 of their papers we have counts for
6 papers
Coded Many-User Multiple Access via Approximate Message Passing
Xiaoqi Liu, Kuan Hsieh, Ramji Venkataramanan
We consider communication over the Gaussian multiple-access channel in the regime where the number of users grows linearly with the codelength. In this regime, schemes based on spa…
Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling
Pablo Pascual Cobo, Kuan Hsieh, Ramji Venkataramanan
We consider the problem of signal estimation in a generalized linear model (GLM). GLMs include many canonical problems in statistical estimation, such as linear regression, phase r…
Near-Optimal Coding for Many-user Multiple Access Channels
Kuan Hsieh, Cynthia Rush, Ramji Venkataramanan
This paper considers the Gaussian multiple-access channel (MAC) in the asymptotic regime where the number of users grows linearly with the code length. We propose efficient coding…
Modulated Sparse Superposition Codes for the Complex AWGN Channel
Kuan Hsieh, Ramji Venkataramanan
This paper studies a generalization of sparse superposition codes (SPARCs) for communication over the complex additive white Gaussian noise (AWGN) channel. In a SPARC, the codebook…
Capacity-achieving Spatially Coupled Sparse Superposition Codes with AMP Decoding
Cynthia Rush, Kuan Hsieh, Ramji Venkataramanan
Sparse superposition codes, also called sparse regression codes (SPARCs), are a class of codes for efficient communication over the AWGN channel at rates approaching the channel ca…
Spatially Coupled Sparse Regression Codes: Design and State Evolution Analysis
Kuan Hsieh, Cynthia Rush, Ramji Venkataramanan
We consider the design and analysis of spatially coupled sparse regression codes (SC-SPARCs), which were recently introduced by Barbier et al. for efficient communication over the…