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.

翻译：在常规的C-RAN中,基带信号在量化/压缩后被传送到中央处理单位,以保持RRH的复杂程度;然而,Fronthaul的能力有限被认为是C-RAN支持大型系统的能力(如大型机器型通信)的一个重大瓶颈。 因此,与传统的C-RAN不同的是,我们提议了一个基于学习的系统,在每次RRH进行检测时都在当地进行,只有向CURH提供可能性信息。为此,我们开发了一个通用的设置理论学方法来估计概率功能。可以使用这种方法将现有的检测方法扩大到C-RAN设置。