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GPU Acceleration of a Configurable N-Way MIMO Detector for Wireless Systems

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Abstract

Multiple-input multiple-output (MIMO) wireless is an enabling technology for high spectral efficiency and has been adopted in many modern wireless communication standards, such as 3GPP-LTE and IEEE 802.11n. However, (optimal) maximum a-posteriori (MAP) detection suffers from excessively high computational complexity, which prevents its deployment in practical systems. Hence, many algorithms have been proposed in the literature that trade-off performance versus detection complexity. In this paper, we propose a flexible N-Way MIMO detector that achieves excellent error-rate performance and high throughput on graphics processing units (GPUs). The proposed detector includes the required QR decomposition step and a tree-search detector, which exploits the massive parallelism available in GPUs. The proposed algorithm performs multiple tree searches in parallel, which leads to excellent error-rate performance at low computational complexity on different GPU architectures, such as Nvidia Fermi and Kepler. We highlight the flexibility of the proposed detector and demonstrate that it achieves higher throughput than existing GPU-based MIMO detectors while achieving the same or better error-rate performance.

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Notes

  1. We assume the reader is familiar with CUDA. A detailed description and explanation can be found in [12].

  2. The serial computations can be handled by any thread. For example, it is possible to always pick the 1st thread to compute the squared ℓ 2 -norm.

  3. The column-norm reordering processing, however, is an effective way of improving the N = 1 case. Nevertheless, the BER performance of the N = 1 case with column norm reordering preprocessing is still worse than that of the N = 2 case without column-norm reordering.

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Acknowledgments

This work was supported in part by Renesas Mobile, Texas Instruments, Xilinx, Samsung, Huawei, and by the US National Science Foundation under grants CNS-1265332, ECCS-1232274, EECS-0925942 and CNS-0923479.

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Authors and Affiliations

  1. Rice University, Houston, TX, 77005, USA

    Michael Wu, Bei Yin, Guohui Wang & Joseph R. Cavallaro

  2. Cornell University, Ithaca, NY, 14850, USA

    Christoph Studer

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  1. Michael Wu
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  2. Bei Yin
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  3. Guohui Wang
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  4. Christoph Studer
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  5. Joseph R. Cavallaro
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Correspondence to Michael Wu.

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Wu, M., Yin, B., Wang, G. et al. GPU Acceleration of a Configurable N-Way MIMO Detector for Wireless Systems. J Sign Process Syst 76, 95–108 (2014). https://doi.org/10.1007/s11265-014-0877-0

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  • DOI: https://doi.org/10.1007/s11265-014-0877-0

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