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.
Multi-cell cooperative processing with limited backhaul traffic is considered for cellular uplinks. To parsimoniously select a set of cooperating base stations, a sparse multi-cell receive-filter is obtained through convex optimization using compressive sensing techniques. Clustered cooperation is also considered, where sparsity is promoted on inter-cluster feedback. A joint equalizer design and dynamic partitioning problem is formulated and solved using an iterative spectral clustering approach. Numerical tests verify the efficacy of proposed methods.