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Decoding of LDPC convolutional codes with rational parity-check matrices from a new graphical perspective

机译:从图形角度看LDPC卷积码与有理奇偶校验矩阵的解码

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Previous studies on low-density parity-check convolutional codes (LDPC-CC) reveal that LDPC-CC with rational parity-check matrices (RPCM) suffer from the unaffordable decoding latency/complexity due to the infinite memory order and the poor bit-error-rate performance due to the existence of length-4 cycles in the Tanner graph. However, in this paper, we show that every LDPC-CC with RPCM can be associated with an equivalent Tanner graph which can avoid the infinite memory order and undesired short length cycles but still implements the same constraints specified by the RPCM. Together with the iterative decoding based on belief propagation with proper scheduling, simulation results indicate that LDPC-CC with RPCM can also provide satisfactory decoding performance.
机译:先前对低密度奇偶校验卷积码(LDPC-CC)的研究表明,由于存储顺序无限和比特错误少,带有有理奇偶校验矩阵(RPCM)的LDPC-CC承受着无法承受的解码延迟/复杂性率性能是由于Tanner图中存在长度为4的循环。但是,在本文中,我们表明,每个具有RPCM的LDPC-CC都可以与等效的Tanner图相关联,该图可以避免无限的存储顺序和不希望的短长度循环,但仍实现由RPCM指定的相同约束。仿真结果表明,结合基于信念传播和适当调度的迭代解码,带有RPCM的LDPC-CC也可以提供令人满意的解码性能。

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