By incorporating cloud computing capabilities to provide radio access functionalities, Cloud Radio Access Networks (CRANs) are considered to be a key enabling technology of future 5G and beyond communication systems. In CRANs, centralized radio resource allocation optimization is performed over a large number of small cells served by simple access points, the Remote Radio Heads (RRHs). However, the fronthaul links connecting each RRH to the cloud introduce delays and entail imperfect Channel

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... te Information (CSI) knowledge at the cloud processors. In order to satisfy the stringent latency requirements envisioned for 5G applications, the concept of Fog Radio Access Networks (FogRANs) has recently emerged for providing cloud computing at the edge of the network. Although FogRAN may alleviate the latency and CSI quality issues of CRAN, its distributed nature degrades network interference mitigation and global system performance. Therefore, we investigate the design of tailored user pre-scheduling and beamforming for FogRANs. In particular, we propose a hybrid algorithm that exploits both the centralized feature of the cloud for globally-optimized pre-scheduling using imperfect global CSIs, and the distributed nature of FogRAN for accurate beamforming with high quality local CSIs. The centralized phase enables the interference patterns over the global network to be considered, while the distributed phase allows for latency reduction, in line with the requirements of FogRAN applications. Simulation results show that our proposed algorithm outperforms the baseline algorithm under imperfect CSIs, jointly in terms of throughput, energy efficiency, as well as delay. key words: 5G and beyond, CRAN, FogRAN, user clustering, beamforming, radio resource management