  Many real-world scenarios for massive machine-type communication involve
sensors monitoring a physical phenomenon. As a consequence, the activity
pattern of these sensors will be correlated. In this letter, we study how the
correlation of user activities can be exploited to improve detection
performance in grant-free random access systems where the users transmit
pilot-hopping sequences and the detection is performed based on the received
energy. We show that we can expect considerable performance gains by adding
regularizers, which take the activity correlation into account, to the
non-negative least squares, which has been shown to work well for independent
user activity.
