Cooperation resource efficient user-centric clustering for QoS provisioning in uplink CoMP

In this paper, user-centric clustering for uplink coordinated multi-point (CoMP) processing in a multi-cell environment is investigated such that the inter-cell interference (ICI) within cluster can be effectively eliminated. Although full cooperation among all cells in the network can achieve the h...

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Bibliographic Details
Published in:SPAWC : signal processing advances in wireless communications pp. 1 - 5
Main Authors: Zhe Zhang, Ning Wang, Jiankang Zhang, Xiaomin Mu, Wong, Kon Max
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Clustering algorithms
Interference
Linear programming
Minimization
Quality of service
Signal processing algorithms
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ISSN:1948-3252
Online Access:Get full text
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Summary:In this paper, user-centric clustering for uplink coordinated multi-point (CoMP) processing in a multi-cell environment is investigated such that the inter-cell interference (ICI) within cluster can be effectively eliminated. Although full cooperation among all cells in the network can achieve the highest cooperative gain in terms of ICI cancellation, such scheme consumes too much cooperation resources in the form of backhaul and processing power. In addition, from the quality-of-service (QoS) provisioning perspective, different users may have different demand for CoMP processing to guarantee its QoS. By considering the tradeoff between cooperative gain and cost, a cluster size minimization problem subject to QoS constraints is formulated to achieve dynamic user-centric clustering for uplink CoMP. As the constrained 0-1 integer programming problem depends largely on the intra-cluster interference weight, we propose a subgradient-based algorithm to solve the relaxed problem. Simulation results demonstrate that the proposed clustering algorithm achieves both the smallest cluster size and the best QoS provisioning for cell-edge users in terms of outage probability in the comparison with existing clustering algorithms.
ISSN:1948-3252
DOI:10.1109/SPAWC.2017.8227663