Receive Beamforming for Gaussian Belief Propagation in Massive Multi-user MIMO for Reducing Fronthaul Bandwidth

We propose two full-digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via Gaussian belief propagation (GaBP) at base stations (BSs) adopting massive multi-input multi-output for open radio access network. In such scenarios, it is vital to reduce the cost of the BSs by limiting the bandwidth of fronthaul (FH) links, and the dimensionality reduction of the received signal based on receive BF at a radio unit is a well-known strategy to reduce the amount of data transported via the FH links. We clarify appropriate criteria for designing a BF weight considering the subsequent GaBP signal detection with the proposed methods: singular-value-decomposition-based BF and QR decomposition-based BF with the aid of discrete-Fourier-transformation-based spreading. Both methods enable dimensionality reduction without compromising the desired signal power by taking advantage of a null space of the channels. BF reduces correlations between the received signals in the BF domain, which improves the robustness of GaBP against spatial fading correlation. Simulation results indicate that the proposed methods improve detection capability while significantly reducing computation.