We investigate the activity detection and channel estimation issues for cell-free Internet of Things (IoT) networks with massive random access. In each time slot, only partial devices are active and communicate with neighboring access points (APs) using non-orthogonal random pilot sequences. Different from the centralized processing in cellular networks, the activity detection and channel estimation in cell-free IoT is more challenging due to the distributed and user-centric architecture. We propose a two-stage approach to detect the random activities of devices and estimate their channel states. In the first stage, the activity of each device is jointly detected by its adjacent APs based on the vector approximate message passing (Vector AMP) algorithm. In the second stage, each AP re-estimates the channel using the linear minimum mean square error (LMMSE) method based on the detected activities to improve the channel estimation accuracy. We derive closed-form expressions for the activity detection error probability and the mean-squared channel estimation errors for a typical device. Finally, we analyze the performance of the entire cell-free IoT network in terms of coverage probability. Simulation results validate the derived closed-form expressions and show that the cell-free IoT significantly outperforms the collocated massive MIMO and small-cell schemes in terms of coverage probability.
翻译:我们调查无细胞Tings(IoT)网络的活动探测和频道估计问题,这些网络有大规模随机访问。在每一个时段,只有部分装置是活跃的,并且使用非垂直随机试验序列与邻近接入点进行通信。与蜂窝网络的集中处理不同,无细胞IoT的活动探测和频道估计由于分布式和以用户为中心的结构而更具挑战性。我们提出一个两阶段办法,以探测装置的随机活动并估计其频道状态。在第一阶段,每个装置的活动都是由邻近APs根据矢量传递的近似信息(Vector AMP)算法联合检测的。在第二阶段,每个AP都根据检测到的提高频道估计准确性的活动,对频道使用线性最小正方差(LMMSE)方法进行重新估计。我们从活动探测误差概率的封闭式表达式表达方式和典型设备的平均对频道估计错误。最后,我们从覆盖的概率的角度分析整个无细胞IOT网络的运行情况。模拟结果,用无细胞MMMFM的大规模表达式表示法验证出,并显示无主位缩缩缩缩缩缩图。