论文标题
聚类用户活动和与MMTC中未知协方差相关的通道的联合估计
Joint Estimation of Clustered User Activity and Correlated Channels with Unknown Covariance in mMTC
论文作者
论文摘要
本文考虑使用\ emph {clustered}用户活动模式中的联合用户识别和通道估计（JUICE）。特别是，我们在与未知的通道协方差矩阵相关的雷利褪色渠道下，在巨大的机器型通信（MMTC）网络中解决了果汁。我们将果汁问题作为最大\ emph {a posteriori}概率（MAP）问题与正确选择的先验，以结合UES聚集活动的部分知识和未知的协方差矩阵。我们根据乘数的交替方向方法（ADMM）得出了一种计算效率的算法，以通过一系列封闭形式更新序列迭代地图问题。数值结果强调了拟议方法在群集用户活动模式的通道估计和活动检测性能方面带来的重大改进。
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.
