Most existing studies on massive grant-free access, proposed to support massive machine-type communications (mMTC) for the Internet of things (IoT), assume Rayleigh fading and perfect synchronization for simplicity. However, in practice, line-of-sight (LoS) components generally exist, and time and frequency synchronization are usually imperfect. This paper systematically investigates maximum likelihood estimation (MLE)-based device activity detection under Rician fading for massive grant-free access with perfect and imperfect synchronization. Specifically, we formulate device activity detection in the synchronous case and joint device activity and offset detection in three asynchronous cases (i.e., time, frequency, and time and frequency asynchronous cases) as MLE problems. In the synchronous case, we propose an iterative algorithm to obtain a stationary point of the MLE problem. In each asynchronous case, we propose two iterative algorithms with identical detection performance but different computational complexities. In particular, one is computationally efficient for small ranges of offsets, whereas the other one, relying on fast Fourier transform (FFT) and inverse FFT, is computationally efficient for large ranges of offsets. The proposed algorithms generalize the existing MLE-based methods for Rayleigh fading and perfect synchronization. Numerical results show the notable gains of the proposed algorithms over existing methods in detection accuracy and computation time.

翻译：针对物联网（IoT）中大规模机器类通信（mMTC）需求提出的大规模免授权接入研究，现有文献为简化分析通常假设瑞利衰落与完美同步。然而在实际场景中，视距（LoS）分量普遍存在，且时间与频率同步往往不完美。本文系统研究了完美与不完美同步条件下莱斯衰落的基于极大似然估计（MLE）的设备活动检测问题。具体而言，我们将同步情形下的设备活动检测以及三种异步情形（即时间异步、频率异步、时间与频率异步）下的联合设备活动与偏移量检测建模为MLE问题。在同步情形中，我们提出一种迭代算法以获取MLE问题的驻点。针对每种异步情形，我们提出两种具有相同检测性能但计算复杂度不同的迭代算法：一种适用于偏移量范围较小时的高效计算场景，另一种基于快速傅里叶变换（FFT）及其逆变换，适用于偏移量范围较大时的高效计算场景。所提算法将现有基于MLE的瑞利衰落与完美同步方法进行了推广。数值结果表明，与现有方法相比，所提算法在检测精度与计算时间方面均具有显著优势。