Dynamic user-centric clustering for uplink cooperation in multi-cell wireless networks

Abstract

In this work, dynamic user-centric cell clustering, which is capable of exploiting dynamics in the channel states, is investigated for joint intra-cluster interference cancellation in a multi-cell environment. A resource efficient cluster size minimization problem is formulated to dynamically group the cells into clusters based on user channel states such that quality-of-service (QoS) provisioning for cell-edge users can be improved. The integer programming problem depends largely on the intra-cluster interference weight. A subgradient algorithm is employed to solve the relaxed problem when no constrain on cooperation cost is present. To reduce extra burden on backhaul due to base station (BS) cooperation, constraints on the number of per-BS cooperative links and the maximum user-centric cluster size are introduced to the optimization problem, which is solved efficiently by a greedy algorithm. Numerical results show that the proposed dynamic user-centric clustering algorithms achieve significant improvements over existing static and fixed-size dynamic clustering schemes in terms of cell-edge performance and backhaul efficiency. The proposed greedy algorithm, in particular, can effectively alleviate the overall and per-BS cooperation cost while guaranteeing the cooperative gain. With similar resource consumption and outage performance, the proposed scheme achieves 12.6% higher rate gain compared with existing fixed size dynamic clustering strategy.

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Identifiers

ORCID for Jiankang Zhang: orcid.org/0000-0001-5316-1711

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