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Multiuser cooperative transmission through superposition modulation based on braid coding

机译:基于辫子编码的叠加调制多用户协作传输

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This paper investigates a cooperative transmission scheme for a multi-source single-destination system through signal-superposition-based braid coding. The source nodes take turns to transmit, and each time, a source “overlays” its new data together with (some or all of) what it overhears from its partner(s) using signal superposition, in a way similar to French-braiding the hair. We demonstrate how the resultant braid coding can be effectively employed in M-to-1 data collection networks to achieve progressive cooperation. We analyze two subclasses of braid coding, the nonregenerative and the regenerative cases, and, using the pairwise error probability (PEP) as a figure of merit, derive the optimal weight parameters theoretically for each class. For the regenerative case, a modified Viterbi maximum-likelihood (ML) estimator is proposed, whose complexity is linear to the message length. We compute the (Euclidean) free distance, and identify the memory size that strikes the best balance between performance and complexity. The proposed cooperative framework based on braid coding is general and subsumes several previous superposition modulation-based cooperative schemes as its special case. Simulations confirm the efficiency of the proposed schemes.
机译:通过基于信号叠加的编织编码,研究了一种多源单目的地系统的协作传输方案。源节点轮流发送,并且每次,源都使用信号叠加将其新数据与(或部分或全部)从伙伴那里听到的信息“叠加”在一起,其方式类似于French-braiding头发。我们演示了如何在M对1数据收集网络中有效地使用所得的辫子编码来实现渐进式协作。我们分析了编织编码的两个子类,即非再生情况和再生情况,并使用成对错误概率(PEP)作为品质因数,从理论上推导了每个类别的最佳权重参数。对于再生情况,提出了一种改进的维特比最大似然(ML)估计器,其复杂度与消息长度成线性关系。我们计算(欧几里得)的自由距离,并确定在性能和复杂性之间取得最佳平衡的内存大小。所提出的基于辫子编码的协作框架是通用的,并且以其先前的特殊情况为基础,包含了几种先前的基于叠加调制的协作方案。仿真证实了所提出方案的效率。

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