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.

翻译：本文针对第六代（6G）无线网络中大规模机器类通信（mMTC）的无源随机接入问题展开研究。首先，我们建立了一种无需额外奇偶校验比特的高效非耦合框架，用于大规模无源随机接入。随后，设计了一种低复杂度的贝叶斯联合解码算法，包含码字检测与拼接两个步骤。具体而言，我们通过利用未知先验条件下的贝叶斯最优无散正交近似消息传递，提出了一种贝叶斯码字检测方法。该方法有效利用输出的长期信道统计信息进行码字拼接，从而恢复原始消息。由于避免了使用奇偶校验比特，频谱效率得以提升。此外，我们从计算复杂度和解码错误概率两方面，对所提出的基于贝叶斯联合解码的大规模非耦合无源随机接入方案进行了性能分析。通过渐近分析，我们获得了一些有助于大规模无源随机接入设计的深刻见解。最后，大量仿真结果验证了该方案在6G无线网络中的有效性。