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

arXiv: https://arxiv.org/abs/2103.11456 · HTML: https://mlsanity.com/paper/2103.11456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Massive Machine-type Communications in 5G: Physical and MAC-layer solutions](https://mlsanity.com/paper/1606.03893/) (2016)
- [Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach](https://mlsanity.com/paper/1711.00355/) (2017)
- [Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul](https://mlsanity.com/paper/1606.02337/) (2016)
- [5G Converged Cell-less Communications in Smart Cities](https://mlsanity.com/paper/1609.06632/) (2016)
- [Online Nonlinear Estimation via Iterative L2-Space Projections: Reproducing Kernel of Subspace](https://mlsanity.com/paper/1712.04573/) (2017)

## Abstract (arXiv)

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.