Model-Based Neural Collaboration Framework for Resource-Efficient Downlink Beamforming in Cell-Free Massive MIMO Systems

To enable improved spectral efficiency and uniform service of cell-free massive multiple-input multiple-output (CF-mMIMO) systems, challenges such as excessive communication overheads for gathering global CSI at the central processor (CP) and high computational complexity for centralized interference management should be addressed. Deep learning (DL)-based decentralized optimization approaches have been proposed to tackle these challenges. However, conventional black-box deep neural network (DNN)-based fronthaul coordination methods require large DNNs and are less adaptable to changes in wireless channel statistics and environments. In this paper, we develop a model-based neural collaboration (NC) framework, which leverages DNN-assisted signal processing built upon our proposed low-complexity distributed weighted minimum mean squared error (L-D-WMMSE) algorithm, for downlink CF-mMIMO beamforming. DNN components for neural initialization (NI) and neural parameterization (NP) reduce the fronthaul overheads while improving the sum-rate of user equipments (UEs). Through our model-based approach, these DNN components are designed to be compact, thereby reducing computational complexity. We derive gradients for DNN updates that each AP can compute using its local CSI and the fronthaul messages exchanged for downlink beamforming. This enables a distributed online DNN adaptation that runs during normal operation of the CF-mMIMO system at zero additional fronthaul overheads.