On the fundamental limits of massive connectivity

This paper aims to provide an information theoretical analysis of massive device connectivity scenario in which a large number of devices with sporadic traffic communicate in the uplink to a base-station (BS). In each coherence time interval, the BS needs to identify the active devices, to estimate their channels, and to decode the transmitted messages from the devices. This paper first derives an information theoretic upper bound on the overall transmission rate. We then provide a degree-of-freedom (DoF) analysis that illustrates the cost of device identification for massive connectivity. We show that the optimal number of active devices is strictly less than half of the coherence time slots, and the achievable DoF decreases linearly with the number of active devices when it exceeds the number of receive antennas. This paper further presents a two-phase practical framework in which device identification and channel estimation are performed jointly using compressed sensing techniques in the first phase, with data transmission taking place in the second phase. We outline the opportunities in utilizing compressed sensing results to analyze the performance of the overall framework and to optimize the system parameters.