2021/03/21 by Daniyal Amir Awan, Renato L. G. Cavalcante, Awan, Daniyal Amir +5
Engineering · #Advanced MIMO Systems Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Wireless Communication Security Techniques
paper · pdf · doi:10.48550/arxiv.2103.11456
openalex publication_date 2021/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In conventional C-RAN, baseband signals are forwarded after quantization/ compression to the central unit for centralized processing to keep the complexity of the RRHs low. However, the limited capacity of the fronthaul is thought to be a significant bottleneck in the ability of C-RAN to support large systems (e.g. massive machine-type communications (mMTC)). Therefore, in contrast to the conventional C-RAN, we propose a learning-based system in which the detection is performed locally at each RRH and only the likelihood information is conveyed to the CU. To this end, we develop a general set-theoretic learningmethod to estimate likelihood functions. The method can be used to extend existing detection methods to the C-RAN setting.