KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Subcarrier and Power Allocation for Multiuser MIMO-OFDM Systems with Various Detectors

  • Mao, Jing (State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University) ;
  • Chen, Chen (State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University) ;
  • Bai, Lin (School of Electronic and Information Engineering, Beihang University) ;
  • Xiang, Haige (State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University) ;
  • Choi, Jinho (School of Information and Communications, Gwangju Institute of Science and Technology (GIST))
  • Received : 2017.02.17
  • Accepted : 2017.06.22
  • Published : 2017.10.31
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Abstract

Resource allocation plays a crucial role in multiuser multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems to improve overall system performance. While previously proposed resource allocation algorithms are mainly designed from the point of view of the information-theoretic, we formulate the resource allocation problem as an average bit error rate (BER) minimization problem subject to a total power constraint when considering employing realistic MIMO detection techniques. Subsequently, we derive the optimal subcarrier and power allocation algorithms for three types of well-known MIMO detectors, including the maximum likelihood (ML) detector, linear detectors, and successive interference cancellation (SIC) detectors. To reduce the complexity, we also propose a two-step suboptimal algorithm that separates subcarrier and power allocation for each detector. We also analyze the diversity gain of the proposed suboptimal algorithms for various MIMO detectors. Simulation results confirm that the proposed suboptimal algorithm for each detector can achieve a comparable performance with the optimal allocation with a much lower complexity. Moreover, it is shown that the suboptimal algorithms perform better than the conventional algorithms that are known in the literature.

Keywords

Acknowledgement

Supported by : National Science Foundation of China (NSFC)

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