Massive MIMO Channel Estimation via Compressed and Quantized Feedback

This paper focuses on downlink channel state information (CSI) acquisition. A frequency division duplex (FDD) of massive MIMO system is considered. In such systems, the base station (BS) obtains the downlink CSI from the mobile users' feedback. A key consideration is to reduce the feedback overhead while ensuring that the BS accurately recovers the downlink CSI. Existing approaches often resort to dictionary-based or tensor/matrix decomposition techniques, which either exhibit unsatisfactory accuracy or induce heavy computational load at the mobile end. To circumvent these challenges, this work formulates the limited channel feedback problem as a quantized and compressed matrix recovery problem. The formulation presents a computationally challenging maximum likelihood estimation (MLE) problem. An ADMM algorithm leveraging existing harmonic retrieval tools is proposed to effectively tackle the optimization problem. Simulations show that the proposed method attains promising channel estimation accuracy, using a much smaller amount of feedback bits relative to existing methods.

I. Introduction

In multiple-input-multiple-output (MIMO) communication systems, downlink channel state information (CSI) at the base station (BS) is vital for many tasks including multiplexing, beamforming, and resource allocation [1], [2]. In the massive MIMO systems, the number of antennas at the base station/mobile users can easily reach tens or hundreds-i.e., the downlink channel matrix can be of very high dimension [1], [3]. Under such circumstances, acquiring accurate downlink CSI at the BS becomes a challenging task. Particularly, in frequency division duplex (FDD) systems, downlink CSI on the BS side is usually acquired from mobile users' feedback [4], [5], as channel reciprocity does not hold in FDD systems. However, transmitting back the full channel matrix to the BS is not viable, as it incurs high feedback overhead; see discussions in [6], [7].