Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems

TL;DR

This paper introduces a set-theoretic learning approach for local detection in cell-less C-RAN systems, reducing fronthaul load by transmitting likelihoods instead of raw signals.

Contribution

It proposes a novel set-theoretic learning method to estimate likelihood functions, enabling local detection and extending existing methods to C-RAN architectures.

Findings

Reduces fronthaul capacity requirements by local detection

Extends detection methods to C-RAN with likelihood estimation

Enhances scalability for large systems like mMTC

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.