  In this paper, we investigate unsourced random access for massive
machine-type communications (mMTC) in the sixth-generation (6G) wireless
networks. Firstly, we establish a high-efficiency uncoupled framework for
massive unsourced random access without extra parity check bits. Then, we
design a low-complexity Bayesian joint decoding algorithm, including codeword
detection and stitching. In particular, we present a Bayesian codeword
detection approach by exploiting Bayes-optimal divergence-free orthogonal
approximate message passing in the case of unknown priors. The output long-term
channel statistic information is well leveraged to stitch codewords for
recovering the original message. Thus, the spectral efficiency is improved by
avoiding the use of parity bits. Moreover, we analyze the performance of the
proposed Bayesian joint decoding-based massive uncoupled unsourced random
access scheme in terms of computational complexity and error probability of
decoding. Furthermore, by asymptotic analysis, we obtain some useful insights
for the design of massive unsourced random access. Finally, extensive
simulation results confirm the effectiveness of the proposed scheme in 6G
wireless networks.
