Beam-On-Graph: Simultaneous Channel Estimation for mmWave MIMO Systems with Multiple Users

This paper is concerned with the channel estimation problem in multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. We develop a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which multiple users estimate their channels at the same time and the required number of channel measurements is adapted to various channel conditions of different users. To achieve this, we represent the beam direction estimation process by a graph, referred to as the beam-on-graph, and associate the channel estimation process with a code-on-graph decoding problem. Specifically, the base station (BS) and each user measure the channel with a series of random combinations of transmit/receive beamforming vectors until the channel estimate converges. As the proposed SWIFT does not adapt the BS's beams to any single user, we are able to estimate all user channels simultaneously. Simulation results show that SWIFT can significantly outperform the existing random beamforming-based approaches, which use a predetermined number of measurements, over a wide range of signal-to-noise ratios and channel coherence time. Furthermore, by utilizing the users' order in terms of completing their channel estimation, our SWIFT framework can infer the sequence of users' channel quality and perform effective user scheduling to achieve superior performance.