论文标题
基于协方差的用户活动检测和新型飞行员设计的渠道估计方法
A Covariance-based User Activity Detection and Channel Estimation Approach with Novel Pilot Design
论文作者
论文摘要
本文研究了未来物联网（IoT）应用程序的庞大机器类型通信（MMTC），其中网络中存在大量的物联网设备，并且每次瞬间都会有一个随机的子集活跃。基于以下事实：如果基站（BS）配备了大量天线，则可以在空间域中准确估算接收信号的协方差矩阵，我们提出了基于协方差的设备活性检测和通道估计策略在大规模的MIMO（多输入多输出）辅助MMTC系统中。对于此策略，首先提供了一种新型的试验序列设计方法，其中每个设备的试点仅由唯一的相位参数确定。然后，通过估计有助于接收的协方差矩阵的活动试验序列的相位参数，提出了有效的算法来检测活动设备，而无需先前有关活动设备总数的信息。最后，鉴于主动设备的估计，基于常规的最小于点误差（MMSE）方法进行通道估计。值得注意的是，我们提出的策略能够以封闭形式获得所有结果，因此与基于迭代算法的现有策略相比，复杂性要低得多，用于设备检测和通道估计。
This paper studies the massive machine-type communications (mMTC) for the future Internet of Things (IoT) applications, where a large number of IoT devices exist in the network and a random subset of them become active at each time instant. Building upon the fact that the covariance matrix of the received signal can be accurately estimated in the spatial domain if the base station (BS) is equipped with a massive number of antennas, we propose a covariance-based device activity detection and channel estimation strategy in a massive MIMO (multiple-input multiple-output) aided mMTC system. For this strategy, a novel approach for the pilot sequence design is first provided, where the pilot of each device is merely determined by a unique phase parameter. Then, by estimating the phase parameters of the active pilot sequences that contribute to the received covariance matrix, an efficient algorithm is proposed to detect the active devices without the prior information about the total number of active devices. At last, given the estimation of active devices, channel estimation is conducted based on the conventional minimum mean-squared error (MMSE) approach. It is worth noting that our proposed strategy is able to obtain all the results in closed-forms, and is thus of much lower complexity compared to the existing strategies that are based on iterative algorithms for device detection and channel estimation.
