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A novel broadcast scheduling strategy using factor graphs and the sum-product algorithm

机译:利用因子图和和积算法的新型广播调度策略

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摘要

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications.
机译:本文提出了一种新颖的自组织分布式算法,该算法仅通过相邻网络站之间的本地协作交互来查找分组无线网络中的广播时间表。受低密度奇偶校验(LDPC)码在差错控制编码领域的巨大成功的启发,我们通过因子图将广播调度问题(BSP)转换为类似LDPC的问题。在提出的算法中,BSP的约束规则被分为许多简单的局部规则,每个局部规则由因子图中的局部处理单元执行。然后,通过使用和积算法迭代优化广播时间表,在本地处理单元之间高效地交换描述每个站将发送数据包的概率的软信息。仿真结果表明,该算法在信道利用率和平均分组时延方面均优于其他现有的中央处理算法。当网络场景非常复杂时,尤其如此。此外,该算法既复杂度低又完全分布式,适合于实际网络应用中的实现。

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