标题： mMTC中具有未知协方差的聚类用户活动和相关信道联合估计 显示英文标题

标题： Joint Estimation of Clustered User Activity and Correlated Channels with Unknown Covariance in mMTC

摘要： 本文研究了在授予免费接入条件下具有\emph{聚类的}用户活动模式的联合用户识别与信道估计算法（JUICE）。 特别地，我们解决了在相关瑞利衰落信道下具有未知信道协方差矩阵的大规模机器类型通信（mMTC）网络中的 JUICE 问题。 我们将 JUICE 问题构造成一个最大\emph{后验的}概率（MAP）问题，并选择合适的先验知识来结合 UE 集群活动的部分已知信息和未知协方差矩阵。 我们基于交替方向乘子法（ADMM）推导了一种计算高效的算法，通过一系列闭式更新迭代求解该 MAP 问题。 数值结果表明，所提出的方法在集群用户活动模式下的信道估计和活动检测性能方面带来了显著改进。 显示英文摘要

摘要： This paper considers joint user identification and channel estimation (JUICE) in grant-free access with a \emph{clustered} user activity pattern. In particular, we address the JUICE in massive machine-type communications (mMTC) network under correlated Rayleigh fading channels with unknown channel covariance matrices. We formulate the JUICE problem as a maximum \emph{a posteriori} probability (MAP) problem with properly chosen priors to incorporate the partial knowledge of the UEs' clustered activity and the unknown covariance matrices. We derive a computationally-efficient algorithm based on alternating direction method of multipliers (ADMM) to solve the MAP problem iteratively via a sequence of closed-form updates. Numerical results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances for clustered user activity patterns.