Device activity detection is one main challenge in grant-free massive access, which is recently proposed to support massive machine-type communications (mMTC). Existing solutions for device activity detection fail to consider inter-cell interference generated by massive IoT devices or important prior information on device activities and inter-cell interference. In this paper, given different numbers of observations and network parameters, we consider both non-cooperative device activity detection and cooperative device activity detection in a multi-cell network, consisting of many access points (APs) and IoT devices. Under each activity detection mechanism, we consider the joint maximum likelihood (ML) estimation and joint maximum a posterior probability (MAP) estimation of both device activities and interference powers, utilizing tools from probability, stochastic geometry, and optimization. Each estimation problem is a challenging non-convex problem, and a coordinate descent algorithm is proposed to obtain a stationary point. Each proposed joint ML estimation extends the existing one for a single-cell network by considering the estimation of interference powers, together with the estimation of device activities. Each proposed joint MAP estimation further enhances the corresponding joint ML estimation by exploiting prior distributions of device activities and interference powers. The proposed joint ML estimation and joint MAP estimation under cooperative detection outperform the respective ones under non-cooperative detection at the costs of increasing backhaul burden, knowledge of network parameters, and computational complexities.
翻译:由于观测和网络参数的数量不同,我们认为,在由许多接入点和IoT装置组成的多细胞网络中,不合作装置活动探测与合作装置活动探测是一项主要挑战。在每一项活动探测机制下,我们考虑了对设备活动和干扰力的联合最大可能性估计和联合最大事后概率估计(MAP),利用概率、随机几何测量和优化等工具;在本文中,考虑到观测和网络参数的不同,我们认为,在多细胞网络中,不合作装置活动探测和合作装置活动探测,包括许多接入点和IoT装置。在每项活动探测机制下,我们考虑了对设备活动和干扰力的联合最大可能性估计和联合最大可能性估计(MAP),同时考虑了对设备活动和干扰力的联合最大估计(MAP),利用概率、随机测量测量和优化等重要工具对细胞间干扰力进行干扰。每个估计都是一个挑战性的、协调的下降算法以获得一个固定点。每一项拟议的联合估算都扩大了单细胞网络的现有评估,考虑对干扰力的估算,同时估算设备活动。每一项拟议的联合估计都进一步增强相应的ML联合估计,通过利用合作性测测算活动之前的测测测测成本,根据联合测测测测测测测活动和各种联合测算活动和权力下的联合估计,从而提高联合测算成本。