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arXiv · 1205.1885

Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness

Yongming Huang·Gan Zheng·Mats Bengtsson·Kai-Kit Wong·Luxi Yang·Bjorn Ottersten
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Abstract

This paper addresses coordinated downlink beamforming optimization in multicell time-division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.

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Yongming Huang, Gan Zheng, Mats Bengtsson, Kai-Kit Wong, Luxi Yang, Bjorn Ottersten. 2012-05-09. Distributed Multicell Beamforming Design Approaching Pareto Boundary with Max-Min Fairness. https://doi.org/10.1109/twc.2012.061912.111751

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