• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1330-1350
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• 2022

As a preprocessing operation of transmitter antennas, the hybrid precoding is restricted by the limited computing resources of the transmitter. Therefore, this paper proposes a novel hybrid precoding that guarantees the communication efficiency with low complexity and a fast computational speed. First, the analog and digital precoding matrix is derived from the maximum eigenvectors of the channel matrix in the sub-connected architecture to maximize the communication rate. Second, the extended power iteration (EPI) is utilized to obtain the maximum eigenvalues and their eigenvectors of the channel matrix, which reduces the computational complexity caused by the singular value decomposition (SVD). Third, the Aitken acceleration method is utilized to further improve the convergence rate of the EPI algorithm. Finally, the hybrid precoding based on the EPI method and the Aitken acceleration algorithm is evaluated in millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) systems. The experimental results show that the proposed method can reduce the computational complexity with the high performance in mmWave massive MIMO systems. The method has the wide application prospect in future wireless communication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2497-2517
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• 2020

For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2C
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• pp.148-154
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• 2010

In this paper, we first review LDPC codes in general and a belief propagation algorithm that works in logarithm domain. LDPC codes, which is chosen 802.11n for wireless local access network(WLAN) standard are required a large number of computation due to large size of coded block and iteration. Therefore, we presented three kinds of low computational algorithm for LDPC codes. First, sequential decoding with partial group is proposed. It has same H/W complexity, and fewer number of iteration's are required at same performance in comparison with conventional decoder algorithm. Secondly, we have apply early stop algorithm. This method is reduced number of unnecessary iteration. Third, early detection method for reducing the computational complexity is proposed. Using a confidence criterion, some bit nodes and check node edges are detected early on during decoding. Through the simulation, we knew that the iteration number are reduced by half using subset algorithm and early stop algorithm is reduced more than one iteration and computational complexity of early detected method is about 30% offs in case of check node update, 94% offs in case of check node update compared to conventional scheme.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.88-94
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• 2009

We propose a scheme which reduce the computational complexity of the lattice reduction (LR) aided detector in MIMO system. The performance of the ML detection algorithm is good but the computational complexity grows exponentially with the number of antenna elements and constellation points. LR aided detector shows the same diversity with the ML scheme with relatively less complexity. But the LR scheme still requires many computations since it involves several iterations of size reduction and column vector exchange. We notice that the LR process depends not on the received signal but only on the channel matrix so we can apply LR process offline and store the results in Look-Up Table (LUT). In this paper we propose an algorithm to generate the LUT which require less memory requirement and we evaluate the performance and complexity of the proposed system. We show that the proposed system requires less computational complexity with similar detection performance compared with the conventional LR aided detector.

• Journal of information and communication convergence engineering
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• v.4 no.2
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• pp.75-78
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• 2006

This work presents a low complexity maximum-likelihood decoder for signal detection in VBLAST-STBC system, which employs non-square O-STBC code rate 3/4. Stacking received symbols from different symbol duration and applying QR decomposition result in the special format of upper triangular matrix R so that the proposed decoder is able to provide not only ML-like BER performance but also very low computational load. The low computational load and ML-like BER performance properties of the proposed decoder are verified by computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4573-4584
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• 2015

The traditional feature extraction algorithms for recognition of communication signals can hardly realize the balance between computational complexity and signals' interclass gathered degrees. They can hardly achieve high recognition rate at low SNR conditions. To solve this problem, a novel feature extraction algorithm based on Holder coefficient was proposed, which has the advantages of low computational complexity and good interclass gathered degree even at low SNR conditions. In this research, the selection methods of parameters and distribution properties of the extracted features regarding Holder coefficient theory were firstly explored, and then interval gray relation algorithm with improved adaptive weight was adopted to verify the effectiveness of the extracted features. Compared with traditional algorithms, the proposed algorithm can more accurately recognize signals at low SNR conditions. Simulation results show that Holder coefficient based features are stable and have good interclass gathered degree, and interval gray relation classifier with adaptive weight can achieve the recognition rate up to 87% even at the SNR of -5dB.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.107-110
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• 2006

This work presents a low complexity maximum-likelihood decoder for signal detection in VBLAST-STBC system, which employs non-square O-STBC code rate 3/4. By stacking received symbols from different received symbolduration and applying QR decomposition resulting the special format of upper triangular matrix R, the proposed decoder is able to provide not only ML-like BER performance but also very low computational load. The low computational load and ML-like BER performance properties of the proposed decoder are verified by computer simulations.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.188-191
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• 2002

To reduce the bit-rate of video sequences by removing temporal redundancy, motion estimation techniques have been developed. However, the high computational complexity of the problem makes such techniques very difficult to be applied to high-resolution applications in a real time environment. For this reason, low computational complexity motion estimation algorithms are viable solutions. If a priori knowledge about the motion of the current block is available before the motion estimation, a better starting point for the search of n optimal motion vector on be selected and also the computational complexity will be reduced. In this paper, we present an adaptive block matching algorithm based on temporal correlations of consecutive image frames that defines the search pattern and the location of initial starting point adaptively to reduce computational complexity. Experiments show that, comparing with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(㏈) better than DS in terms of PSNR and improves as much as 50% in terms of the average number of search points per motion estimation.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.838-845
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• 2007

In this paper, we propose a modified cubic convolution scaler for the enlargement or reduction of digital images. The proposed method has less computational complexity than the cubic convolution method. In order to reduce the computational complexity, we use the linear function of the cubic convolution and the difference value of adjacent pixels for selecting interpolation methods. We employ adders and barrel shifts to calculate weights of the proposed method. The proposed method is compared with the conventional one for the computational complexity and the image quality. It has been designed and verified by HDL(Hardware Description Language), and synthesized using Xilinx Virtex FPGA.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.56-58
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• 2004

In this paper, we propose an adaptive beamforming algorithm for array antenna. The proposed beamforming algorithm is based on EDS (Euclidean Direction Search) algorithm. Generally LMS algorithm has a much slower rate of convergence, but its low computational complexity and robustness make it a representative method of adaptive beamforming. Although the RLS algorithm is known for its fast convergence to the optimal Wiener solution, it still suffers from high computational complexity and poor performance. The proposed EDS algorithm has a rapid convergence better than LMS algorithm, and has a computational more simple complexity than RLS algorithm. In this paper we compared the efficiency of the EDS algorithm with a standard LMS algorithm.