MLE-based Device Activity Detection for Grant-free Massive Access under Frequency Offsets

Grant-free access is recently proposed as an essential technique for supporting massive machine-type communications (mMTC) for the Internet of Things (IoT). However, high accuracy and low complexity device activity detection under imperfect frequency synchronization remains open. To address this issue, this paper proposes a maximum likelihood estimation (MLE)- based device activity detection method for the frequency asynchronous case. First, we formulate the estimation of device activities together with the device carrier frequency offsets (CFOs) as an MLE problem. Then, to tackle this challenging nonconvex problem, we propose a computationally efficient iterative algorithm using the block coordinate descent (BCD) method and fast computation enabled by fast Fourier transform (FFT). Analytical and 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.