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Generalized sphere packing bound: Basic principles

机译:广义球堆积约束:基本原理

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Kulkarni and Kiyavash recently introduced a new method to establish upper bounds on the size of deletion-correcting codes. This method is based upon tools from hypergraph theory. The deletion channel is represented by a hypergraph whose edges are the deletion balls (or spheres), so that a deletion-correcting code becomes a matching in this hypergraph. Consequently, a bound on the size of such a code can be obtained from bounds on the matching number of a hypergraph. Classical results in hypergraph theory are then invoked to compute an upper bound on the matching number as a solution to a linear-programming problem: the problem of finding fractional transversals. The method by Kulkarni and Kiyavash can be applied not only for the deletion channel but also for other channels, and in particular for those where the error spheres sizes are not all the same. This paper studies this method in its most general setup. We first show that if the error channel is regular and symmetric then the upper bound by this method coincides with the well-known sphere packing bound and thus is called here the generalized sphere packing bound. Even though this bound is explicitly given by a linear programming problem, finding its exact value may still be a challenging task. The art of finding the exact upper bound or slightly weaker ones is the assignment of weights to the hypergraph's vertices in a way that the satisfy the constraints in the linear programming problem. Every valid assignment yields an upper bound and the goal is to find assignments that provide strong upper bounds. We show that for graphs which satisfy a monotonicity property it is possible to find a general formula for such an assignment. Lastly, in order to simplify the complexity of the linear programming, we present a technique based upon graph automorphisms that in many cases can significantly reduce the number of variables and constraints in the linear programming problem. All of our results will be demonstrated an- calculated for the Z channel which will be a case study in our work.
机译:Kulkarni和Kiyavash最近推出了一种新方法,可以确定删除纠正码大小的上限。该方法基于超图理论的工具。删除通道由一个超图表示,该图的边缘是删除球(或球体),因此删除校正代码在该超图中成为匹配项。因此,可以根据超图的匹配数的界限来获得这种代码的大小的界限。然后,调用超图理论中的经典结果来计算匹配数的上限,以解决线性规划问题:找到分数乘积问题。 Kulkarni和Kiyavash的方法不仅可以应用于删除通道,而且可以应用于其他通道,尤其是对于误差范围大小不尽相同的通道。本文以其最通用的设置来研究此方法。我们首先显示,如果误差通道是规则且对称的,则此方法的上限与众所周知的球体填充边界一致,因此在此称为广义球体填充边界。即使此界限是由线性编程问题明确给出的,但要找到其确切值仍可能是一项艰巨的任务。找到精确的上限或稍弱的上限的技术是,以满足线性规划问题中的约束的方式,将权重分配给超图的顶点。每个有效的分配都会产生一个上限,目标是找到能够提供强大上限的分配。我们表明,对于满足单调性的图,可以找到这种分配的一般公式。最后,为了简化线性规划的复杂性,我们提出了一种基于图自同构的技术,该技术在许多情况下可以显着减少线性规划问题中的变量和约束数量。我们将针对Z通道计算出所有结果,并将其作为案例研究。

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