  Massive machine-type communication (mMTC) has been regarded as one of the
most important use scenarios in the fifth generation (5G) and beyond wireless
networks, which demands scalable access for a large number of devices. While
grant-free random access has emerged as a promising mechanism for massive
access, its potential has not been fully unleashed. Particularly, the two key
tasks in massive access systems, namely, user activity detection and data
detection, were handled separately in most existing studies, which ignored the
common sparsity pattern in the received pilot and data signal. Moreover, error
detection and correction in the payload data provide additional mechanisms for
performance improvement. In this paper, we propose a data-assisted activity
detection framework, which aims at supporting more active users by reducing the
activity detection error, consisting of false alarm and missed detection
errors. Specifically, after an initial activity detection step based on the
pilot symbols, the false alarm users are filtered by applying energy detection
for the data symbols; once data symbols of some active users have been
successfully decoded, their effect in activity detection will be resolved via
successive pilot interference cancellation, which reduces the missed detection
error. Simulation results show that the proposed algorithm effectively
increases the activity detection accuracy, and it is able to support $\sim
20\%$ more active users compared to a conventional method in some sample
scenarios.
