Multi-Branch Deep Residual Learning for Clustering and Beamforming in User-Centric Network

In existing works of deep learning-based resource allocation, the scalability degrades heavily with the increase of network complexity, which is due to their limited learning ability of shallow neural networks and insufficient knowledge of network. Nowadays, to address the growth of cell density, cooperative beamforming in user-centric network (UCN) is emerged, where the additional degrees of freedom of multi-antenna and cell coordination aggravate the challenges. This letter proposes a deep residual learning framework, UcnBeamNet, to enhance the ability of approximating the iterative algorithm for sum rate maximization, where multi- branch subnets are connected in parallel to extract extra information. Specifically, a weighted minimum mean square error (WMMSE)-based algorithm is derived to determine the optimal clusters and beamforming matrices; then UcnBeamNet is trained to learn the input-output mapping and provide direct insight of UCN from association matrices in addition to plural inputs. Extensive experiments demonstrate UcnBeamNet still reaches 90.38% sum-rate relative to conventional algorithm even with a large network size, and achieves more than 50,000x speed up in computational efficiency.