Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems
Abstract
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.
Cite
@article{arxiv.2103.11456,
title = {Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems},
author = {Daniyal Amir Awan and Renato L. G. Cavalcante and Zoran Utkovski and Slawomir Stanczak},
journal= {arXiv preprint arXiv:2103.11456},
year = {2021}
}
Comments
Published in 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)