Add to ORKGAbstract—For the additive Gaussian noise channel with aver-age codeword power constraint, sparse superposition codes and adaptive successive decoding is developed. Codewords are linear combinations of subsets of vectors, with the message indexed by the choice of subset. A feasible decoding algorithm is presented. Communication is reliable with error probability exponentially small for all rates below the Shannon capacity. I
This paper studies a generalization of sparse superposition codes (SPARCs) for communication over th...
Sparse superposition codes, also referred to as sparse regression codes (SPARCs), are a class of cod...
We introduce a new algorithm for realizing maximum likelihood (ML) decoding for arbitrary codebooks ...
Abstract—For the additive white Gaussian noise channel with average codeword power constraint, spars...
Abstract—For the additive white Gaussian noise channel with average codeword power constraint, new c...
Sparse superposition codes were recently introduced by Barron and Joseph for reliable communication ...
A method for power- and bandwidth efficient communication over the Gaussian channel is presented. Th...
Sparse superposition codes were recently introduced by Barron and Joseph for reliable communication ...
Sparse superposition codes, or sparse regression codes, constitute a new class of codes, which was f...
Sparse superposition (SS) codes were originally proposed as a capacity-achieving communication schem...
Abstract—Superposition codes are efficient for the Additive White Gaussian Noise channel. We provide...
Sparse superposition codes, or sparse regression codes (SPARCs), are a recent class of codes for rel...
This paper studies the performance of block coding on an additive white Gaussian noise channel under...
Polyanskiy Abstract—It is demonstrated that codewords of good codes for the additive white Gaussian ...
Sparse regression codes (SPARCs) are a recently introduced coding scheme for the additive white Gaus...
This paper studies a generalization of sparse superposition codes (SPARCs) for communication over th...
Sparse superposition codes, also referred to as sparse regression codes (SPARCs), are a class of cod...
We introduce a new algorithm for realizing maximum likelihood (ML) decoding for arbitrary codebooks ...
Abstract—For the additive white Gaussian noise channel with average codeword power constraint, spars...
Abstract—For the additive white Gaussian noise channel with average codeword power constraint, new c...
Sparse superposition codes were recently introduced by Barron and Joseph for reliable communication ...
A method for power- and bandwidth efficient communication over the Gaussian channel is presented. Th...
Sparse superposition codes were recently introduced by Barron and Joseph for reliable communication ...
Sparse superposition codes, or sparse regression codes, constitute a new class of codes, which was f...
Sparse superposition (SS) codes were originally proposed as a capacity-achieving communication schem...
Abstract—Superposition codes are efficient for the Additive White Gaussian Noise channel. We provide...
Sparse superposition codes, or sparse regression codes (SPARCs), are a recent class of codes for rel...
This paper studies the performance of block coding on an additive white Gaussian noise channel under...
Polyanskiy Abstract—It is demonstrated that codewords of good codes for the additive white Gaussian ...
Sparse regression codes (SPARCs) are a recently introduced coding scheme for the additive white Gaus...
This paper studies a generalization of sparse superposition codes (SPARCs) for communication over th...
Sparse superposition codes, also referred to as sparse regression codes (SPARCs), are a class of cod...
We introduce a new algorithm for realizing maximum likelihood (ML) decoding for arbitrary codebooks ...