论文标题
联合通道估计，活动检测和解码使用动态消息安排机器类型通信
Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications
论文作者
论文摘要
在这项工作中，我们为大型机器类型通信提供了联合信道估计，活动检测和数据解码方案。通过在因子图中包括通道和先验活动因子，我们提出了双线性消息 - 安排GAMP（BIMSGAMP），这是一种消息串联解决方案，该解决方案使用通道解码器信念来完善活动检测和数据解码。我们包括两个基于剩余信念传播和活动用户检测的策略，在每个迭代中都评估和安排了消息的活动。对Bimsgamp的收敛性以及对其计算复杂性的研究进行了分析。数值结果表明，BIMSGAMP的表现优于最先进的算法，从而突出了通过使用动态调度策略和系统中通道解码部分的效果来实现的收益。
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.
