Splitting Precoding with Subspace Selection and Quantized Refinement for Massive MIMO

Limited fronthaul capacity is a practical bottleneck in massive multiple-input multiple-output (MIMO) 5G architectures, where a base station (BS) consists of an advanced antenna system (AAS) connected to a baseband unit (BBU). Conventional downlink designs perform all precoding at the BBU and transmit a high-dimensional precoding matrix over the fronthaul, resulting in significant quantization loss and signaling overhead. This letter proposes a splitting precoding architecture that separates the design between the AAS and BBU. The AAS performs local subspace selection, after which the BBU computes a quantization-aware refinement precoding over the resulting reduced-dimensional effective channel. Numerical results show that the proposed splitting precoding strategy achieves higher sum rate than conventional one-stage precoding.