Improved Devices Activity Detection for Grant-Free Random Access in Cell-Free Massive MIMO Systems

Supporting massive machine type communications (mMTC) has been one of the key concerns in the next generation wireless network, where a large number of devices access the network. The cell-free (CF) massive MIMO is introduced as a promising enabling technology for mMTC. In this paper, we focus on the device activity detection (AD) for grant-free random access in CF massive MIMO systems. First, the CF massive random access signal model for mMTC scenarios is given, and we formulate the devices AD as a maximum likelihood estimation (MLE) problem by leveraging the block diagonal property of the covariance matrix of the received signals. Next, we propose an improved device AD performance algorithm, which consists of a serial processing algorithm and a parallel processing algorithm. The parallel processing algorithm can further reduce the computational overhead. In addition, through the asymptotic analysis of the MLE via its associated Fisher information matrix and solving a quadratic programming problem, we derive a theoretical prediction bound of devices AD performance, which can numerically predict theoretical results of devices AD with the coordinate descent algorithm. Finally, simulation results show that the proposed AD scheme can achieve a better performance-complexity trade-off than existing schemes, and the theory prediction bound can match well with the simulation of devices AD as the antenna number increases.