Locally Orthogonal Training Design for Cloud-RANs Based on Graph Coloring

TL;DR

This paper proposes a scalable training sequence design for Cloud-RANs using local orthogonality modeled as a graph coloring problem, significantly reducing training overhead in large networks.

Contribution

It introduces the concept of local orthogonality for training sequences and models it as a graph coloring problem, providing a scalable solution for large-scale CRANs.

Findings

Training length scales with log of number of users

Proposed scheme outperforms existing methods

Scalable design reduces training overhead

Abstract

We consider training-based channel estimation for a cloud radio access network (CRAN), in which a large amount of remote radio heads (RRHs) and users are randomly scattered over the service area. In this model, assigning orthogonal training sequences to all users will incur a substantial overhead to the overall network, and is even impossible when the number of users is large. Therefore, in this paper, we introduce the notion of local orthogonality, under which the training sequence of a user is orthogonal to those of the other users in its neighborhood. We model the design of locally orthogonal training sequences as a graph coloring problem. Then, based on the theory of random geometric graph, we show that the minimum training length scales in the order of $\ln K$, where $K$ is the number of users covered by a CRAN. This indicates that the proposed training design yields a scalable solution to sustain the need of large-scale cooperation in CRANs. Numerical results show that the proposed scheme outperforms other reference schemes.