Search Results - sub-linear decoding complexity

SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes by Lee, Kangwook, Chandrasekher, Kabir, Pedarsani, Ramtin, Ramchandran, Kannan

“… In this paper, we design group testing algorithms for approximate recovery with order-optimal sample complexity by leveraging design and analysis tools from modern sparse-graph coding theory…”
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Neighbor discovery for wireless networks via compressed sensing by Zhang, Lei, Luo, Jun, Guo, Dongning

“…This paper studies the problem of neighbor discovery in wireless networks, namely, each node wishes to discover and identify the network interface addresses…”
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Sub-Linear Time Support Recovery for Compressed Sensing Using Sparse-Graph Codes by Li, Xiao, Yin, Dong, Pawar, Sameer, Pedarsani, Ramtin, Ramchandran, Kannan

“… Our key contribution is a new compressed sensing framework through a new family of carefully designed sparse measurement matrices associated with minimal measurement costs and a low-complexity recovery algorithm…”
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Sub-linear Time Stochastic Threshold Group Testing via Sparse-Graph Codes by Reisizadeh, Amirhossein, Abdalla, Pedro, Pedarsani, Ramtin

“…). We leverage tools and techniques from sparse-graph codes and propose a fast decoding algorithm for stochastic threshold group testing…”
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Sub-linear time compressed sensing using sparse-graph codes by Xiao Li, Pawar, Sameer, Ramchandran, Kannan

“… ) is sub-linear in N for some 0 <; δ <; 1. A new family of sparse measurement matrices is introduced with a low-complexity recovery algorithm, which achieves a sub-linear measurement cost O(K log 1.3̇ N…”
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List-decoding using the XOR lemma by Trevisan, L.

“…/) encoding time, and probabilistic 0/sup /spl tilde//(n) list-decoding time. (Note that the decoding time is sub-linear in the length of the encoding…”
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Recovering K-sparse N-length vectors in O(K log N) time: Compressed sensing using sparse-graph codes by Xiao Li, Ramchandran, Kannan

“…) measurements with a computational complexity of O(K log N). Both the measurement cost and algorithm runtime are order-optimal for support recovery when K = O (Nδ ) for some 0 <; δ <; 1…”
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Sub-linear time compressed sensing for support recovery using left and right regular sparse-graph codes by Vem, Avinash, Janakiraman, Nagaraj Thenkarai, Narayanan, Krishna

“… (sub-linear time complexity when K is sub-linear in N). We show that by replacing the left-regular ensemble with left and right regular ensemble, we can reduce…”
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A fast Hadamard transform for signals with sub-linear sparsity by Scheibler, Robin, Haghighatshoar, Saeid, Vetterli, Martin

“…A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT…”
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A Fast Hadamard Transform for Signals with Sub-linear Sparsity in the Transform Domain by Scheibler, Robin, Haghighatshoar, Saeid, Vetterli, Martin

“…A new iterative low complexity algorithm has been presented for computing the Walsh-Hadamard transform (WHT) of an \(N…”
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Sub-linear Time Support Recovery for Compressed Sensing using Sparse-Graph Codes by Li, Xiao, Yin, Dong, Pawar, Sameer, Pedarsani, Ramtin, Ramchandran, Kannan

“… sparse measurement matrices associated with minimal measurement costs and a low-complexity recovery algorithm…”
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Efficient Algorithms for Noisy Group Testing by Sheng Cai, Jahangoshahi, Mohammad, Bakshi, Mayank, Jaggi, Sidharth

“…Group-testing refers to the problem of identifying (with high probability) a (small) subset of D defectives from a (large) set of N items via a "small" number…”
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FFAST: An Algorithm for Computing an Exactly k -Sparse DFT in O( k\log k) Time by Pawar, Sameer, Ramchandran, Kannan

“… better? We show that asymptotically in k and n, when k is sub-linear in n (precisely, k = O(n δ ), where 0 ≤ δ <; 1), and the support of the non-zero DFT coefficients is uniformly random, the fast fourier aliasing-based sparse transform…”
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Applications of Coding Theory to Sub-Linear Time Sparse Recovery Problems by Thenkarai Janakiraman, Nagaraj

“…) algorithm and the iterative hard decision decoding of product codes. We show that the FFAST algorithm is analogous to an iterative decoder for a carefully defined…”
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Group Testing using left-and-right-regular sparse-graph codes by Vem, Avinash, Janakiraman, Nagaraj T, Narayanan, Krishna R

“… of \(\epsilon\)). More importantly the iterative decoding algorithm has a sub…”
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Fast sparse 2-D DFT computation using sparse-graph alias codes by Ong, Frank, Pawar, Sameer, Ramchandran, Kannan

“…) noiseless spatial-domain measurements in O(k log k) computational time. Our results are attractive when the sparsity is sub-linear with respect to the signal dimension, that is, when k → ∞ and k/N → 0…”
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Neighbor Discovery for Wireless Networks via Compressed Sensing by Zhang, Lei, Luo, Jun, Guo, Dongning

“…This paper studies the problem of neighbor discovery in wireless networks, namely, each node wishes to discover and identify the network interface addresses…”
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Asynchronous Massive Access and Neighbor Discovery Using OFDMA by Chen, Xu, Liu, Lina, Guo, Dongning, Wornell, Gregory W

“… and message decoding with a codelength that is O…”
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Computing a k-sparse n-length Discrete Fourier Transform using at most 4k samples and O(k log k) complexity by Pawar, Sameer, Ramchandran, Kannan

“… better? We show that asymptotically in k and n, when k is sub-linear in n (i.e., k ∝ n δ where 0 <; δ <; 1), and the support of the non-zero DFT coefficients is uniformly random, we can exploit this sparsity in two fundamental ways…”
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GROTESQUE: Noisy Group Testing (Quick and Efficient) by Sheng Cai, Jahangoshahi, Mohammad, Bakshi, Mayank, Jaggi, Sidharth

“…Group-testing refers to the problem of identifying (with high probability) a (small) subset of D defectives from a (large) set of N items via a "small" number…”
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