Li Chen
Sun Yat-sen University
72 Papers
239 Citations
Li Chen is an academic researcher from Sun Yat-sen University. The author has contributed to research in topics: Decoding methods & Reed–Solomon error correction. The author has an hindex of 12, co-authored 72 publications. Previous affiliations of Li Chen include Newcastle University.
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Papers
Designing Protograph-Based Quasi-Cyclic Spatially Coupled LDPC Codes With Large Girth
TL;DR: This paper introduces a systematic design to eliminate 4-cycles in a coupled protograph and introduces a procedure for constructing QC-SC-LDPC codes of girth at least eight, which can be interpreted as a multi-stage graph lifting process that yields a greater flexibility in designing QC- SC- LDPC codes with a large girth than previous approaches.
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Progressive Algebraic Soft-Decision Decoding of Reed-Solomon Codes
Li Chen,Siyun Tang,Xiao Ma +2 more
TL;DR: A progressive ASD (PASD) algorithm that enables the conventional ASD algorithm to perform decoding with an adjustable designed factorization output list size (OLS) leading to an incremental computation for the interpolation and an enhanced error-correction capability.
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Iterative Soft Decoding of Reed-Solomon Convolutional Concatenated Codes
TL;DR: A novel iterative soft decoding algorithm for the concatenated code, aiming to better exploit its error-correction potential, is proposed and its advantage over the existing decoding algorithms is demonstrated.
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Spatially Coupled LDPC Codes via Partial Superposition and Their Application to HARQ
TL;DR: The proposed BMST-LDPC codes are a special class of BMST codes, which have lower error floors even with an encoding memory of one, inheriting a low decoding latency and is integrated with the hybrid automatic retransmission request (HARQ) over the block fading channel, improving the throughput performance.
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Performance of Reed--Solomon codes using the Guruswami--Sudan algorithm with improved interpolation efficiency
TL;DR: A complexity analysis is presented comparing the Guruswami-Sudan (GS) algorithm with the modified GS algorithm, showing the modification can reduce complexity significantly in low error weight situations.
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