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Low Complexity User Scheduling, Ordering and Transmit Covariance Matrix Optimization Algorithms for Successive Zero-Forcing Precoding

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Abstract

In this paper we consider user scheduling, ordering and transmit covariance matrix optimization problems under successive zero-forcing (SZF) precoding for multiuser multiple-input multiple-output downlink. We propose a heuristic user scheduling metric and an intermediate user grouping technique to develop a low complexity greedy scheduling algorithm. A suboptimal user ordering technique is also proposed for transmit covariance matrix optimization under SZF. Proposed algorithm is of low complexity, but performs closely to the highly complex exhaustive search algorithm. For transmit covariance optimization under SZF, a dirty paper coding based algorithm has been previously proposed, which is computationally very complex. In this paper, we propose a suboptimal but much simplified algorithm, which employs an iterative procedure similar to a known multiple access channel (MAC) covariance optimization algorithm, but does not involve multiple levels of covariance matrix transformations. With the proposed suboptimal user ordering the exhaustive search through all possible user orders is avoided during transmit covariance matrix optimization resulting in a significant complexity reduction, and without a significant performance penalty. Simulation results show that the proposed algorithm performs very close to the known algorithm in the low SNR region.

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Notes

  1. Part of this work was presented in [14]. This paper expands on that work providing a substantial revision to the proposed algorithm description and results.

  2. We note here that the optimization of the numbers of transmitted streams to the users will result in improved sum rate. However, the algorithm to achieve that will be more complex. Analysis of performance and complexity of such technique is an interesting problem for future work. However, it is beyond the scope of this paper. On the other hand, different numbers of receive antennas for different users can also be considered with the proposed algorithm (again assuming the number of transmitted streams equal to the number of receive antennas).

  3. Note that generally user scheduling algorithm is used to select the users to be served in a given time slot, and then optimization of the transmit covariance matrices for the selected users is performed. Therefore, in that case the user order will not have to be re-computed as in [15], since the proposed user selection algorithm would have already determined the user order as explained in Section 3.

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Acknowledgments

Funding for this work has been provided by TRLabs, TELUS, Huawei Technologies, the Rohit Sharma Professorship, MITACS, and the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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Authors and Affiliations

  1. Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Shreeram Sigdel & Witold A. Krzymień

  2. TRLabs, Edmonton, AB, Canada

    Shreeram Sigdel & Witold A. Krzymień

Authors
  1. Shreeram Sigdel
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  2. Witold A. Krzymień
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Corresponding author

Correspondence to Witold A. Krzymień.

Additional information

This work was in part presented at and published in the proceedings of the 69th IEEE Vehicular Technology Conference (VTC’09-Spring), Barcelona, Spain, April 2009, and the 10th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC’09), Perugia, Italy, June 2009.

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Sigdel, S., Krzymień, W.A. Low Complexity User Scheduling, Ordering and Transmit Covariance Matrix Optimization Algorithms for Successive Zero-Forcing Precoding. Wireless Pers Commun 75, 2467–2484 (2014). https://doi.org/10.1007/s11277-013-1477-1

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