Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Efficient User Selection Algorithms for Multiuser MIMO Systems with Zero-Forcing Dirty Paper Coding

  • Wang, Youxiang (Department of Information and Communication Engineering, Yeungnam University) ;
  • Hur, Soo-Jung (Department of Information and Communication Engineering, Yeungnam University) ;
  • Park, Yong-Wan (Department of Information and Communication Engineering, Yeungnam University) ;
  • Choi, Jeong-Hee (School of Computer and Communication Engineering, Daegu University)
  • Received : 2009.10.01
  • Accepted : 2010.05.04
  • Published : 2011.06.30
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Abstract

This paper investigates the user selection problem of successive zero-forcing precoded multiuser multiple-input multiple-output (MU-MIMO) downlink systems, in which the base station and mobile receivers are equipped with multiple antennas. Assuming full knowledge of the channel state information at the transmitter, dirty paper coding (DPC) is an optimal precoding strategy, but practical implementation is difficult because of its excessive complexity. As a suboptimal DPC solution, successive zero-forcing DPC (SZF-DPC) was recently proposed; it employs partial interference cancellation at the transmitter with dirty paper encoding. Because of a dimensionality constraint, the base station may select a subset of users to serve in order to maximize the total throughput. The exhaustive search algorithm is optimal; however, its computational complexity is prohibitive. In this paper, we develop two low-complexity user scheduling algorithms to maximize the sum rate capacity of MU-MIMO systems with SZF-DPC. Both algorithms add one user at a time. The first algorithm selects the user with the maximum product of the maximum column norm and maximum eigenvalue. The second algorithm selects the user with the maximum product of the minimum column norm and minimum eigenvalue. Simulation results demonstrate that the second algorithm achieves a performance similar to that of a previously proposed capacity-based selection algorithm at a high signal-to-noise (SNR), and the first algorithm achieves performance very similar to that of a capacity-based algorithm at a low SNR, but both do so with much lower complexity.

Keywords

References

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