Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering

TL;DR

This paper proposes a sparsity-regularized approach for multi-cell cooperation in cellular uplinks, reducing backhaul traffic by dynamically forming cooperation clusters using spectral clustering and group-sparse regression.

Contribution

It introduces a novel dynamic clustered cooperation method that jointly determines sparse equalizers and cooperation clusters, with decentralized implementations for scalability and robustness.

Findings

Effective reduction in backhaul traffic demonstrated

Dynamic clustering improves cooperation efficiency

Decentralized algorithms enhance scalability and robustness

Abstract

Multi-cell cooperative processing with limited backhaul traffic is studied for cellular uplinks. Aiming at reduced backhaul overhead, a sparsity-regularized multi-cell receive-filter design problem is formulated. Both unstructured distributed cooperation as well as clustered cooperation, in which base station groups are formed for tight cooperation, are considered. Dynamic clustered cooperation, where the sparse equalizer and the cooperation clusters are jointly determined, is solved via alternating minimization based on spectral clustering and group-sparse regression. Furthermore, decentralized implementations of both unstructured and clustered cooperation schemes are developed for scalability, robustness and computational efficiency. Extensive numerical tests verify the efficacy of the proposed methods.