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arXiv 2202.11278cs.ITmath.IT
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基于动态消息调度的机器型通信信道估计、活动检测和解码联合算法

Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications

R. B. Di Renna, R. C. de Lamare

摘要:

在这项工作中,我们提出了一个联合信道估计,活动检测和数据解码方案的大规模机器类型的通信。通过在因子图中包含信道和先验活动因子,我们提出了双线性消息调度GAMP(BiMSGAMP),这是一种使用信道解码器信念来改进活动检测和数据解码的消息传递解决方案。我们包括两个消息调度策略的基础上,剩余的信念传播和活动用户检测的消息进行评估和调度,在每一个新的迭代。分析了BiMSGAMP的收敛性,并对其计算复杂性进行了研究。数值结果表明,BiMSGAMP优于国家的最先进的算法,突出的增益所取得的使用动态调度策略和信道解码部分在系统中的效果。

英文摘要:

In this work, we present a joint channel estimation, activity detection and data decoding scheme for massive machine-type communications. By including the channel and the a priori activity factor in the factor graph, we present the bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that uses the channel decoder beliefs to refine the activity detection and data decoding. We include two message-scheduling strategies based on the residual belief propagation and the activity user detection in which messages are evaluated and scheduled in every new iteration. An analysis of the convergence of BiMSGAMP along with a study of its computational complexity is carried out. Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms, highlighting the gains achieved by using the dynamic scheduling strategies and the effects of the channel decoding part in the system.

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