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.
Compared to our conference precursor @cite_25 , decentralized implementation of the proposed distributed and clustered cooperation schemes is developed in the present work. Decentralized computation of spatial equalizers and clusters makes MCP scalable to large networks, robust to isolated points of failure, and more resource-efficient than centralized implementation, since pieces of the overall problem are solved concurrently at different BSs, coordinated by peer message exchanges. Decentralized implementation of the component algorithms, such as group-sparse regression, eigenvector computation for network Laplacians, and k-means clustering, is also discussed. In addition, extensive simulations are performed to verify the performance, including the use of multiple antennas, user fairness, and dynamic clustered cooperation scenarios.
@cite_25
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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.