Enhanced Pilot Design for Collision-Resilient Unsourced Random Access in Massive MIMO

In unsourced random access (URA) system, user pilot collisions are a key challenge when multiple users select identical pilot sequences, leading to poor activity detection and decoding performance. To address this issue, this paper develops a two-stage segmented pilot (TSSP) design for collision-resilient URA in massive multiple-input multiple-output (MIMO) system. The proposed scheme enables a substantial enlargement of the effective pilot space without increasing the physical codebook size and detection complexity. First, we construct a probabilistic collision model by leveraging the birthday-problem analogy, which provides theoretical characterization of collision events and closely matches the simulation results. Second, we propose a segmented pilot mapping and decoding framework in which the pilot bits are divided into two interconnected parts linked by common pilot bits. This structured segmentation allows the system to resolve partial collisions and reduce missed detection in high-density access scenarios. Third, we develop a joint-covariance linear minimum mean square error (JC-LMMSE) scheme that exploits the segmented pilot structure to suppress pilot contamination and improve estimation accuracy. Monte Carlo simulations show that the proposed TSSP framework achieves up to 3.5 dB gain in collision scenarios and approximately 1 dB gain in collision-free cases over existing methods, while maintaining comparable computational complexity.