Composite Preambles Based on Differential Phase Rotations for Grant-Free Random Access Systems

Abstract

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Grant-Free Random Access Enhanced by Massive MIMO and Non-Orthogonal Preambles

“…[14] proposed two unique preamble designs, which are the single orthogonal preamble and the concatenated orthogonal preamble in grant-free RA utilizing massive MIMO for the concurrent detection of users and estimation of channels. In [15], a composite preamble was designed by utilizing differential phase rotations for grant-free RA, achieved through the aggregation of orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase shifts. This approach aims to minimize the likelihood of preamble collisions, thus ultimately enhancing the performance of automatic user detection and channel estimation.…”