Massive Device Connectivity with Massive MIMO
Wei Yu (Univerity of Toronto)
In this talk, we consider a massive device communications scenario in which a large number of devices need to connect to a base-station in the uplink, but user traffic is sporadic so that at any given coherence time only a subset of users are active. For such a system, user activity detection and channel estimation are key issues. This talk presents a two-phase framework in which compressed sensing techniques are used in the first phase to identify the devices and their channels, while data transmission takes place in the second phase. We propose the use of approximate message passing (AMP) for device identification and show that the state evolution can be used to analytically characterize the missed detection and false alarm probabilities in AMP. This talk further considers the massive connectivity problem in the massive MIMO regime. We analytically show that massive MIMO can significantly enhance user activity detection, but the non-orthogonality of pilot sequences can nevertheless introduce significant channel estimation error, hence limiting the overall rate. We quantify this effect and characterize the optimal pilot length for massive uncoordinated device access.