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arXiv 2602.08952cs.ITmath.IT
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基于团的删除纠正码:通过惩罚引导的团搜索

Clique-Based Deletion-Correcting Codes via Penalty-Guided Clique Search

Indian Institute of Technology Dharwad(达瓦德印度理工学院)

Aniruddh Pandav, Rajshekhar V Bhat

AI总结:

本文提出了一种基于团的删除纠正码构造方法,通过惩罚引导的团搜索算法,实现了在有限长度范围内更优的码本大小,并改进了解码复杂度。

摘要:

我们研究了通过将问题公式化为最大团问题(MCP)来构建d-删除纠正二进制码。在此公式化中,顶点代表候选码字,边连接那些最长公共子序列(LCS)距离保证能纠正最多d次删除的码字对。一个有效的码本对应于所得到图中的一个团,寻找最大的码本等同于确定最大团。虽然之前已经探讨了基于MCP的删除纠正码的公式化,但我们证明应用惩罚引导的团搜索(PGCS),一种受动态局部搜索(DLS)启发的轻量级随机团搜索启发式方法,能够为块长n=8,9,…,14和删除参数d=1,2,3的情况,持续产生比现有基于图的启发式方法,包括最小度和着色方法更大的码本。在几个有限长度范围内,所得到的码本匹配已知的最佳大小,并且优于经典的构造,如Helberg码。对于在分段接收下的解码,其中码字边界已知,我们提出了一种优化的LCS基于解码器,该解码器利用符号计数过滤和提前终止来显著减少LCS评估的数量,同时保持精确解码保证。这些优化导致平均情况下解码复杂度显著低于基线O(|C|n²)方法。

英文摘要:

We study the construction of $d$-deletion-correcting binary codes by formulating the problem as a Maximum Clique Problem (MCP). In this formulation, vertices represent candidate codewords and edges connect pairs whose longest common subsequence (LCS) distance guarantees correction of up to $d$ deletions. A valid codebook corresponds to a clique in the resulting graph, and finding the largest codebook is equivalent to identifying a maximum clique. While MCP-based formulations for deletion-correcting codes have previously been explored, we demonstrate that applying Penalty-Guided Clique Search (PGCS), a lightweight stochastic clique-search heuristic inspired by Dynamic Local Search (DLS), consistently yields larger codebooks than existing graph-based heuristics, including minimum-degree and coloring methods, for block lengths $n = 8,9,\dots,14$ and deletion parameters $d = 1,2,3$. In several finite-length regimes, the resulting codebooks match known optimal sizes and outperform classical constructions such as Helberg codes. For decoding under segmented reception, where codeword boundaries are known, we propose an optimized LCS-based decoder that exploits symbol-count filtering and early termination to substantially reduce the number of LCS evaluations while preserving exact decoding guarantees. These optimizations lead to significantly lower average-case decoding complexity than the baseline $O(|C| n^2)$ approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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