Tensor-Based Unified Joint Channel Estimation and Active Device Detection Scheme for High-Mobility Grant-Free Random Access Scenarios

With the rapid development of Internet of Things (IoT), efficient and reliable massive IoT device connections need to be widely supported in the upcoming next-generation communication networks, especially for emerging high-mobility scenarios. In this context, this article investigates massive grant-free random access (GF-RA) in high mobility scenarios, focusing on active device detection (ADD) and channel estimation (CE) under fast time-varying channels. By exploiting the inherent low-rank structure of the observed pilot-signal-tensor, a tensor-based GF-RA transmission scheme is provided. On this basis, we propose a joint ADD and CE method based on the canonical polyadic (CP) model for both sourced and unsourced RA frameworks. More specifically, by remodelling the observation signal as a third-order tensor, the channel parameters can be grouped in the factor matrices of the CP model. However, the excessive number of potential device connections in massive GF-RA scenarios lead to excessively large dimensions of the factor matrices, thus resulting in severe ill-condition. To solve this problem, the Vandermonde structure of factor matrices is developed, which enables the effective exploitation of the tensor subspace for CP decomposition. Then, by utilizing the preallocated training precoders, an effective two-dimensional search method is proposed to jointly detect active devices and initialize the iterative estimation of channel parameters. Finally, due to the grouping situation, independent and coupled channel parameters are estimated by appropriate methods based on maximum likelihood (ML) and iterative updating, respectively. Moreover, the preallocation of training precoders can be unified to the unsourced RA scenarios, where the joint ADD and CE can be regard as a simple degenerate method compared to sourced RA. Simulation results demonstrate that the proposed tensor-based GF-RA framework outperforms the state-of-the-art schemes in terms of both ADD and CE performance.