Low-Complexity Joint Estimation for Asynchronous Massive Internet of Things: An ADMM Approach

Abstract

In this paper, low-complexity algorithms based alternating direction method of multiplier (ADMM) framework are proposed for joint channel estimation, activity detection and delay detection in asynchronous massive Internet of Things (IoT). In particular, an asynchronous frame structure with a maximum tolerable delay is used to model the sporadic transmissions in IoT. Ajoint estimation is formulated as a recovery problem for sparse signals. Taking advantage of the special sparse structure, we propose a linear ADMM algorithm for single-antenna estimation and a two-stage ADMM algorithm for multi-antenna estimation without any prior information, respectively. In addition, the computation complexity of the proposed algorithms is analyzed. Normalized mean square error, false alarm rate, and missed alarm rate are discussed to measure the accuracy of the proposed algorithms. The simulation results show that the proposed algorithms have superior performance in terms of recovery accuracy, activity detection, and computation efficiency. More importantly, the proposed algorithms have strong robustness to delay.