Sparse superposition codes: A practical approach
Sparse superposition codes: A practical approach
复制标题
稀疏叠加码：一种实用方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
W. Gross
中科院分区：
文献类型：
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作者：
C. Condo;W. Gross
稀疏叠加码是一类容量实现码，其解码可以被解释为压缩感知问题。近似消息传递算法在压缩感知中被证明是有效的，已经以不同的形式被提出作为一种有效的解码方法。然而，大多数文献集中在无限的代码长度和渐近性能，而对矩阵和向量操作的强烈依赖表明，面向硬件的方法可能更有效。本文分析了两种有限码长定点精度译码算法的性能：5比特码字符号量化导致的性能下降≤ 0.15 dB。在算法量化值的建议，连同代码的建设和算法的近似，导致可以忽略不计的性能下降。在选择一组代码作为案例研究后，进行了解码复杂度估计，表明完全并行的架构是不可行的。对部分并行的解决方案给出了建议和改进。
Sparse Superposition Codes are a class of capacity achieving codes for which decoding can be interpreted as a compressive sensing problem. The approximate message passing algorithm, proven to be effective in compressive sensing, has been proposed in different incarnations as a valid decoding approach. However, most literature focuses on infinite code length and asymptotic performance, while the strong reliance on matrix-and vector-wise operations suggests that a hardware-oriented approach might be more efficient. This work analyzes the performance of two decoding algorithms with finite code lengths and fixed point precision: 5-bit codeword symbol quantization is shown to cause performance degradation ≤ 0.15 dB. In-algorithm quantization values are proposed, together with code construction and algorithm approximations that cause negligible performance degradation. After selecting a set of codes as a case study, a decoding complexity estimation is performed, demonstrating that a fully parallel architecture is unfeasible. Suggestions and improvements towards partially-parallel solutions are given.
影响因子：
2.5
作者：
Rush, Cynthia;Greig, Adam;Venkataramanan, Ramji
通讯作者：
Venkataramanan, Ramji