论文标题
ML的估计和图估计，对设备活动的设备活动中的无授予随机访问中的估计和估计
ML Estimation and MAP Estimation for Device Activities in Grant-Free Random Access with Interference
论文作者
论文摘要
设备活动检测是无授予随机访问的主要挑战，最近提议支持大规模机器类型通信（MMTC）的大规模访问。现有的解决方案未能考虑大规模物联网（IoT）设备或有关设备活动和干扰的重要信息产生的干扰。在本文中，我们考虑了在其他细胞中大量设备产生的干扰下，在接入点（AP）上的设备活动检测。我们考虑关节最大似然（ML）估计值和关节最大值是对设备活动和干扰力的后验概率（MAP）估计，从而共同利用工具从概率，随机几何和优化中使用工具。每个估计问题都是凸（DC）编程问题的差异，并提出了坐标下降算法以获得固定点。提出的ML估计通过考虑干扰能力的估计以及设备活动的估计来扩展现有的ML估计。提出的地图估计通过利用设备活动和干扰力的先前分布来进一步增强了提出的ML估计。数值结果表明了拟议的联合估计设计的大量收益，并揭示了明确考虑干扰的重要性以及在设备活动检测中的先验信息的价值。
Device activity detection is one main challenge in grant-free random access, which is recently proposed to support massive access for massive machine-type communications (mMTC). Existing solutions fail to consider interference generated by massive Internet of Things (IoT) devices, or important prior information on device activities and interference. In this paper, we consider device activity detection at an access point (AP) in the presence of interference generated by massive devices from other cells. We consider the joint maximum likelihood (ML) estimation and the joint maximum a posterior probability (MAP) estimation of both the device activities and interference powers, jointly utilizing tools from probability, stochastic geometry and optimization. Each estimation problem is a difference of convex (DC) programming problem, and a coordinate descent algorithm is proposed to obtain a stationary point. The proposed ML estimation extends the existing ML estimation by considering the estimation of interference powers together with the estimation of device activities. The proposed MAP estimation further enhances the proposed ML estimation by exploiting prior distributions of device activities and interference powers. Numerical results show the substantial gains of the proposed joint estimation designs, and reveal the importance of explicit consideration of interference and the value of prior information in device activity detection.
