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[Keyword] computational complexity reduction(4hit)

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  • Peak Cancellation Signal Generation Considering Variance in Signal Power among Transmitter Antennas in PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Signals Open Access

    Jun SAITO  Nobuhide NONAKA  Kenichi HIGUCHI  

     
    PAPER-Wireless Communication Technologies

      Vol:
    E107-B No:10
      Page(s):
    661-669

    We propose a novel peak-to-average power ratio (PAPR) reduction method based on a peak cancellation (PC) signal vector that considers the variance in the average signal power among transmitter antennas for massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) signals using the null space in a MIMO channel. First, we discuss the conditions under which the PC signal vector achieves a sufficient PAPR reduction effect after its projection onto the null space of the MIMO channel. The discussion reveals that the magnitude of the correlation between the PC signal vector before projection and the transmission signal vector should be as low as possible. Based on this observation and the fact that to reduce the PAPR it is helpful to suppress the variation in the transmission signal power among antennas, which may be enhanced by beamforming (BF), we propose a novel method for generating a PC signal vector. The proposed PC signal vector is designed so that the signal power levels of all the transmitter antennas are limited to be between the maximum and minimum power threshold levels at the target timing. The newly introduced feature in the proposed method, i.e., increasing the signal power to be above the minimum power threshold, contributes to suppressing the transmission signal power variance among antennas and to improving the PAPR reduction capability after projecting the PC signal onto the null space in the MIMO channel. This is because the proposed method decreases the magnitude of the correlation between the PC signal vectors before its projection and the transmission signal vectors. Based on computer simulation results, we show that the PAPR reduction performance of the proposed method is improved compared to that for the conventional method and the proposed method reduces the computational complexity compared to that for the conventional method for achieving the same target PAPR.

  • Parallel Peak Cancellation Signal-Based PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Transmission Open Access

    Taku SUZUKI  Mikihito SUZUKI  Kenichi HIGUCHI  

     
    PAPER-Wireless Communication Technologies

      Pubricized:
    2020/11/20
      Vol:
    E104-B No:5
      Page(s):
    539-549

    This paper proposes a parallel peak cancellation (PC) process for the computational complexity-efficient algorithm called PC with a channel-null constraint (PCCNC) in the adaptive peak-to-average power ratio (PAPR) reduction method using the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. By simultaneously adding multiple PC signals to the time-domain transmission signal vector, the required number of iterations of the iterative algorithm is effectively reduced along with the PAPR. We implement a constraint in which the PC signal is transmitted only to the null space in the MIMO channel by beamforming (BF). By doing so the data streams do not experience interference from the PC signal on the receiver side. Since the fast Fourier transform (FFT) and inverse FFT (IFFT) operations at each iteration are not required unlike the previous algorithm and thanks to the newly introduced parallel processing approach, the enhanced PCCNC algorithm reduces the required total computational complexity and number of iterations compared to the previous algorithms while achieving the same throughput-vs.-PAPR performance.

  • Complexity-Reduced Adaptive PAPR Reduction Method Using Null Space in MIMO Channel for MIMO-OFDM Signals Open Access

    Taku SUZUKI  Mikihito SUZUKI  Yoshihisa KISHIYAMA  Kenichi HIGUCHI  

     
    PAPER-Wireless Communication Technologies

      Pubricized:
    2020/03/23
      Vol:
    E103-B No:9
      Page(s):
    1019-1029

    This paper proposes a computational complexity-reduced algorithm for an adaptive peak-to-average power ratio (PAPR) reduction method previously developed by members of our research group that uses the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. The proposed algorithm is an extension of the peak cancellation (PC) signal-based method that has been mainly investigated for per-antenna PAPR reduction. This method adds the PC signal, which is designed so that the out-of-band radiation is removed/reduced, directly to the time-domain transmission signal at each antenna. The proposed method, referred to as PCCNC (PC with channel-null constraint), performs vector-level signal processing in the PC signal generation so that the PC signal is transmitted only to the null space in the MIMO channel. We investigate three methods to control the beamforming (BF) vector in the PC signal, which is a key factor in determining the achievable PAPR performance of the algorithm. Computer simulation results show that the proposed PCCNC achieves approximately the same throughput-vs.-PAPR performance as the previous method while dramatically reducing the required computational cost.

  • Effective Frame Selection for Blind Source Separation Based on Frequency Domain Independent Component Analysis

    Yusuke MIZUNO  Kazunobu KONDO  Takanori NISHINO  Norihide KITAOKA  Kazuya TAKEDA  

     
    PAPER-Engineering Acoustics

      Vol:
    E97-A No:3
      Page(s):
    784-791

    Blind source separation is a technique that can separate sound sources without such information as source location, the number of sources, and the utterance content. Multi-channel source separation using many microphones separates signals with high accuracy, even if there are many sources. However, these methods have extremely high computational complexity, which must be reduced. In this paper, we propose a computational complexity reduction method for blind source separation based on frequency domain independent component analysis (FDICA) and examine temporal data that are effective for source separation. A frame with many sound sources is effective for FDICA source separation. We assume that a frame with a low kurtosis has many sound sources and preferentially select such frames. In our proposed method, we used the log power spectrum and the kurtosis of the magnitude distribution of the observed data as selection criteria and conducted source separation experiments using speech signals from twelve speakers. We evaluated the separation performances by the signal-to-interference ratio (SIR) improvement score. From our results, the SIR improvement score was 24.3dB when all the frames were used, and 23.3dB when the 300 frames selected by our criteria were used. These results clarified that our proposed selection criteria based on kurtosis and magnitude is effective. Furthermore, we significantly reduced the computational complexity because it is proportional to the number of selected frames.