A Fully Bayesian Approach for Massive MIMO Unsourced Random Access

In this paper, we propose a novel fully Bayesian approach for the massive multiple-input multiple-output (MIMO) unsourced random access (URA). The payload of each user device is coded by sparse regression codes (SPARCs) without redundant parity bits. A model established to capture probabilistic characteristics overall system. Particularly, adopt core idea model-based learning establish flexible channel adapt complex environments. Different from traditional divide-and-conquer or pilot-based MIMO URA strategies, three-layer message passing (TLMP) algorithm jointly decode all information blocks, as well acquire channel, which adopts variational and approximate passing. We verify that our proposed TLMP significantly enhances spectral efficiency compared with state-of-the-arts baselines, more robust possible codeword collisions.