  In the rapidly evolving landscape of 5G and beyond 5G (B5G) mobile cellular
communications, efficient data compression and reconstruction strategies become
paramount, especially in massive multiple-input multiple-output (MIMO) systems.
A critical challenge in these systems is the capacity-limited fronthaul,
particularly in the context of the Ethernet-based common public radio interface
(eCPRI) connecting baseband units (BBUs) and remote radio units (RRUs). This
capacity limitation hinders the effective handling of increased traffic and
data flows. We propose a novel two-stage compression approach to address this
bottleneck. The first stage employs sparse Tucker decomposition, targeting the
weight tensor's low-rank components for compression. The second stage further
compresses these components using complex givens decomposition and run-length
encoding, substantially improving the compression ratio. Our approach
specifically targets the Zero-Forcing (ZF) beamforming weights in BBUs. By
reconstructing these weights in RRUs, we significantly alleviate the burden on
eCPRI traffic, enabling a higher number of concurrent streams in the radio
access network (RAN). Through comprehensive evaluations, we demonstrate the
superior effectiveness of our method in Channel State Information (CSI)
compression, paving the way for more efficient 5G/B5G fronthaul links.
