协同数据交换中的迭代归并算法
Iterative Merging Algorithm for Cooperative Data Exchange
摘要：
我们考虑的问题，找到最小的和率策略的合作数据交换系统，不允许分组分裂（NPS-CDE）。在NPS-CDE系统中，存在多个地理位置接近的协作客户端，它们发送数据包以帮助其他客户端恢复数据包集。最小和速率策略是以最小和速率（传输总数）实现通用恢复（所有客户端恢复整个分组集的情况）的策略。我们提出了一种迭代合并（IM）算法，递归合并客户端集的基础上，较低的估计最小和率和更新的值的最小和率。我们还表明，一个最小的总和率策略可以通过分配率的本地恢复在每个合并的客户端设置在IM算法中学习。实验表明，当客户端数目小于$94$时，该算法的复杂度低于现有的确定性算法。
英文摘要：
We consider the problem of finding the minimum sum-rate strategy in cooperative data exchange systems that do not allow packet-splitting (NPS-CDE). In an NPS-CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on a lower estimate of the minimum sum-rate and updates to the value of the minimum sum-rate. We also show that a minimum sum-rate strategy can be learned by allocating rates for the local recovery in each merged client set in the IM algorithm. We run an experiment to show that the complexity of the IM algorithm is lower than that of the existing deterministic algorithm when the number of clients is lower than $94$.
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