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

Device Activity and Embedded Information Bit Detection Using AMP in Massive MIMO