使用左右正则稀疏图码的分组测试
Group Testing using left-and-right-regular sparse-graph codes
摘要：
我们考虑的问题，非自适应组测试的$N$项目，其中$K$或更少的项目是已知的缺陷。我们提出了一种基于左右正则稀疏图码的测试方案和一个简单的迭代译码器。我们表明，对于任何任意小的$ε>0$，我们的计划只需要$m=c_εK\log \frac{c_1N}{K}$测试，以恢复$(1-ε)$分数的缺陷项目具有高概率（w.h.p），即，其中概率在$N$和$K$中渐近地接近$1$，其中常数$c_ε$和$\ell$的值是期望的误差平层$ε$和常数$c_1=\frac{\ell}{c_ε}$的函数（对于$ε$的各种值，观察到近似等于1）。更重要的是，迭代解码算法具有$\mathcal{O}(K\log \frac{N}{K})$的次线性计算复杂度，这已知是最优的。同样对于$m=c_2 K\log K\log \frac{N}{K}$测试，我们的方案恢复了\textit{whole}组缺陷项w.h. p。这些结果对于问题的无噪声和有噪声版本都是有效的，只要缺陷项的数量与项目的总数成次线性比例，即，$K=o(N)$。仿真结果验证了理论结果，显示出所需的测试的数量相比，基于左正则稀疏图的测试方案有很大的改善。
英文摘要：
We consider the problem of non-adaptive group testing of $N$ items out of which $K$ or less items are known to be defective. We propose a testing scheme based on left-and-right-regular sparse-graph codes and a simple iterative decoder. We show that for any arbitrarily small $ε>0$ our scheme requires only $m=c_εK\log \frac{c_1N}{K}$ tests to recover $(1-ε)$ fraction of the defective items with high probability (w.h.p) i.e., with probability approaching $1$ asymptotically in $N$ and $K$, where the value of constants $c_ε$ and $\ell$ are a function of the desired error floor $ε$ and constant $c_1=\frac{\ell}{c_ε}$ (observed to be approximately equal to 1 for various values of $ε$). More importantly the iterative decoding algorithm has a sub-linear computational complexity of $\mathcal{O}(K\log \frac{N}{K})$ which is known to be optimal. Also for $m=c_2 K\log K\log \frac{N}{K}$ tests our scheme recovers the \textit{whole} set of defective items w.h.p. These results are valid for both noiseless and noisy versions of the problem as long as the number of defective items scale sub-linearly with the total number of items, i.e., $K=o(N)$. The simulation results validate the theoretical results by showing a substantial improvement in the number of tests required when compared to the testing scheme based on left-regular sparse-graphs.
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