Simulation study reveals a 6 dB energy efficiency gain in mmWave unsourced random access, indicating improved performance across doubly-selective mobile communication channels.
Time-frequency doubly-selective characteristics are often exhibited by wireless transmission channels, due to hardware imperfections and the relative mobility of devices. This paper investigates a massive unsourced random access (URA) scheme for the millimeter-wave (mmWave) band in the presence of a doubly-selective channel. By utilizing the sparse path and beam aggregation properties of mmWave communications, the equivalent channel can be modeled as a multi-dimensional block-sparse matrix. This modeling is achieved in the code-time-frequency (CTF)-angle domain through segment-based compressed coding. The joint channel estimation and segment data recovery is then achieved using a pattern-coupled sparse Bayesian learning (PCSBL) with generalized approximate message passing (GAMP) algorithm. A clustering method based on similar channel features is proposed to concatenate different data segments. Numerical simulation results validate the effectiveness of the proposed scheme, demonstrating a 6 dB increase in energy efficiency compared to tree-based coded compressed sensing (CCS) scheme.
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Hu et al. (2025) studied this question.
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