Fronthaul Data Reduction in Massive MIMO Aided C-RAN via Two-timescale Hybrid Compression

Abstract

In massive MIMO aided cloud radio access network (C-RAN), plenty of remote radio heads (RRHs), each equipped with a massive MIMO array, are distributed within a specific geographical area and are connected to a centralized baseband unit (BBU) pool through fronthaul links. One major performance bottleneck in the uplink of massive MIMO aided C-RAN is that, the RRHs need to transport a huge amount of data to the BBU for baseband processings. Existing fronthaul compression methods that rely on fully-digital processing are not suitable for the massive MIMO regime due to their high implementation cost. To overcome this challenge, we propose a two-timescale hybrid analog-and-digital spatial compression scheme at RRHs to reduce the fronthaul data, where the analog filter is updated at a slow timescale according to the channel statistics to achieve massive MIMO array gain, and the digital filter is updated at a fast timescale according to the instantaneous effective channel state information (CSI) to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters. Simulations verify the advantages of the proposed scheme over state-of-the-art baseline schemes. © 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved.