MLE-based Device Activity Detection for Grant-free Massive Access under Rician Fading

Abstract

Recently, grant-free access is proposed as an essential technique for supporting massive machine-type communications (mMTC) for the Internet of Things (IoT). Most existing studies on device activity detection either make no use of channel statistics or assume Rayleigh fading for simplicity. Device activity detection under more general fading models remains open. To shed some light, this paper considers Rician fading and proposes a maximum likelihood estimation (MLE)-based device activity detection method. First, we formulate the estimation of device activities as an MLE problem. Then, based on the coordinate descent (CD) method, we develop an iterative algorithm, where all coordinate optimization problems are solved analytically, to obtain a stationary point of the non-convex MLE problem. Finally, numerical results demonstrate the notable gains of the proposed method over the existing solutions and offer important design insights into practical massive grant-free access for mMTC. The results in this paper generalize those for Rayleigh fading and have practical sense.