Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering
This work addresses backhaul constraints in cellular networks, offering incremental improvements in cooperative processing efficiency.
The paper tackles the problem of multi-cell cooperative processing with limited backhaul traffic in cellular uplinks by designing sparsity-regularized receive filters and dynamic clustered cooperation, achieving reduced backhaul overhead as verified through extensive numerical tests.
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.