Generalized channel estimation and user detection for massive connectivity with mixed-ADC massive MIMO

Ting Liu, Shi Jin, Chao-Kai Wen, Michail Matthaiou, Xiaohu You

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
477 Downloads (Pure)


This paper aims to provide a partial-DFT pilot sequence assisted joint channel estimation and user activity detection scheme for massive connectivity, in which a large number
of devices with sporadic transmission communicate with a base station (BS) in the uplink. The joint channel estimation and device detection problem can be formulated as a compressed sensing single measurement vector (SMV) or multiple measurement vector (MMV) problem depending on whether the BS is equipped with single or large number of antennas. Due to high hardware cost and power consumption in massive multiple-input multiple-output (MIMO) systems, a mixed analog-to-digital converter (ADC) architecture is considered. In order to accommodate a large number of simultaneously transmitting devices, the joint channel estimation and active user detection are formulated as a MMV problem for the massive connectivity scenario; and the proposed GTurbo-MMV algorithm can precisely estimate the channel state information (CSI) and detect active devices with relatively low overhead. Furthermore, we study the state evolution (SE) for the MMV problem to obtain achievable bounds on channel estimation and device detection performance, in which both the missing and false detection probabilities can be made tend to zero in the massive MIMO regime. Simulation results confirm the theoretical accuracy of our analysis.
Original languageEnglish
JournalIEEE Transactions on Wireless Communications
Early online date26 Apr 2019
Publication statusEarly online date - 26 Apr 2019


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