  In this paper, we investigate an unsourced random access scheme for massive
machine-type communications (mMTC) in the sixth-generation (6G) wireless
networks with sporadic data traffic. Firstly, we establish a general framework
for massive unsourced random access based on a two-layer signal coding, i.e.,
an outer code and an inner code. In particular, considering Rician fading in
the scenario of mMTC, we design a novel codeword activity detection algorithm
for the inner code of unsourced random access based on the distribution of
received signals by exploiting the maximum likelihood (ML) method. Then, we
analyze the performance of the proposed codeword activity detection algorithm
exploiting Fisher Information Matrix, which facilitates the derivative of the
approximated distribution of the estimation error of the codeword activity
vector when the number of base station (BS) antennas is sufficiently large.
Furthermore, for the outer code, we propose an optimization algorithm to
allocate the lengths of message bits and parity check bits, so as to strike a
balance between the error probability and the complexity required for outer
decoding. Finally, extensive simulation results validate the effectiveness of
the proposed detection algorithm and the optimized length allocation scheme
compared with an existing detection algorithm and a fixed length allocation
scheme.
