Abstract | ||
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In this paper, we present a class of hybrid coupled serially concatenated codes (HC-SCC), in which coupling is achieved by re-encoding partial outputs from both the outer and the inner encoders. Firstly, we derive the exact density evolution (DE) equations over the binary erasure channels (BECs) for the HC-SCCs, which can be used to analyze the impact of the coupling ratios and the coupling memories on the performances of the HC-SCCs. Secondly, we present a genie-aided lower bound for the proposed HC-SCCs to estimate their error-floors. Thirdly, we use the DE equations to design randomly punctured HC-SCCs, including rate-compatible HC-SCCs. The analytical and the numerical results show that: 1) for comparable coupling memories, the iterative decoding thresholds of the proposed HC-SCC ensembles are better than those of the recently introduced spatially coupled serially concatenated code (SC-SCC) ensembles and the partially parity-coupled turbo code (PPC-TC) ensembles; 2) with outer coupling memory five and inner coupling memory five, the iterative decoding thresholds of the rate-compatible HC-SCCs over the BECs are 0.0003 away from the corresponding channel capacities for a wide range of coding rates; 3) simulation results over the additive white Gaussian noise (AWGN) channels and the BECs are provided to confirm the performance advantages of the HC-SCCs. Particularly, the simulation results show that the HC-SCCs admit lower error-floor than the SC-SCCs and the rate-compatible HC-SCCs perform better than the PPC-TCs, which are consistent with the results of DE analysis. |
Year | DOI | Venue |
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2022 | 10.1109/TCOMM.2022.3175525 | IEEE Transactions on Communications |
Keywords | DocType | Volume |
Binary erasure channel (BEC),density evolution (DE),parity re-encoding,serially concatenated code (SCC),spatial coupling | Journal | 70 |
Issue | ISSN | Citations |
7 | 0090-6778 | 1 |
PageRank | References | Authors |
0.37 | 37 | 3 |
Name | Order | Citations | PageRank |
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Chaojie Yang | 1 | 1 | 0.37 |
Shancheng Zhao | 2 | 1 | 0.71 |
Xiao Ma | 3 | 487 | 64.77 |