  Future cellular networks will support a massive number of devices as a result
of emerging technologies such as Internet-of-Things and sensor networks.
Enhanced by machine type communication (MTC), low-power low-complex devices in
the order of billions are projected to receive service from cellular networks.
Contrary to traditional networks which are designed to handle human driven
traffic, future networks must cope with MTC based systems that exhibit sparse
traffic properties, operate with small packets and contain a large number of
devices. Such a system requires smarter control signaling schemes for efficient
use of system resources. In this work, we consider a grant-free random access
cellular network and propose an approach which jointly detects user activity
and single information bit per packet. The proposed approach is inspired by the
approximate message passing (AMP) and demonstrates a superior performance
compared to the original AMP approach. Furthermore, the numerical analysis
reveals that the performance of the proposed approach scales with number of
devices, which makes it suitable for user detection in cellular networks with
massive number of devices.
