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.

翻译：在5G及超5G（B5G）移动蜂窝通信的快速演进中，高效的数据压缩与重构策略变得至关重要，尤其是在大规模多输入多输出（MIMO）系统中。这些系统面临的关键挑战是容量受限的前传链路，特别是连接基带处理单元（BBU）与远端射频单元（RRU）的基于以太网的通用公共无线电接口（eCPRI）。该容量限制阻碍了日益增长的业务量与数据流的高效处理。我们提出了一种新颖的两阶段压缩方法来解决这一瓶颈。第一阶段采用稀疏Tucker分解，针对权重张量的低秩分量进行压缩；第二阶段利用复数Givens分解与游程编码进一步压缩这些分量，从而显著提升压缩比。我们的方法专门针对BBU中的迫零（ZF）波束赋形权重进行优化。通过在RRU中重构这些权重，我们大幅减轻了eCPRI链路的传输压力，并使无线接入网（RAN）中并发数据流数目得以增加。通过全面评估，我们验证了该方法在信道状态信息（CSI）压缩方面的卓越性能，为构建更高效的5G/B5G前传链路铺平了道路。